JP6771966B2 - Magnetic resonance imaging device and image processing device - Google Patents

Description

Embodiments of the present invention relate to magnetic resonance imaging devices and image processing devices.

In recent years, in the field of neuroscience, elucidation of the region of brain function localization and the connection between the regions has been promoted by using images captured by a magnetic resonance imaging apparatus. For example, a method has been proposed in which the presence or absence of a disease is determined by analyzing the difference from a normal brain regarding a predetermined parameter such as the brightness value of an image for each region of brain function localization.

Zhou J1, Gennatas ED, Kramer JH, Miller BL, Seeley WW, "Predicting regional neurodegeneration from the healthy brain functional connectome", Neuron. 2012 Mar 22; 73 (6): 1216-27 Susumu Mori, Kenichi Oishi, Andrea V. Faria, Michael I. Miller3, "Atlas-Based Neuroinformatics via MRI: Harnessing Information from Past Clinical Cases and Quantitative Image Analysis for Patient Care", Annual Review of Biomedical Engineering, Vol.15: 71 -92

An object to be solved by the present invention is to provide a magnetic resonance imaging apparatus and an image processing apparatus capable of supporting image analysis of a plurality of regions in the brain.

The Magnetic Resonance Imaging (MRI) apparatus according to the embodiment includes a specific unit and a search unit. Based on the analysis result of the image of the subject and the degree of attention set for each of the plurality of regions in the brain, the specific unit identifies the search start position of the matrix showing the connection relationship between the regions related to the plurality of regions. To do. The search unit searches the matrix using the search start position.

FIG. 1 is a diagram showing a configuration example of an MRI apparatus according to the first embodiment. FIG. 2 is a diagram for explaining a region of brain function localization. FIG. 3 is a diagram for explaining a nerve fiber bundle. FIG. 4 is a diagram for explaining the hierarchical structure of the cortical region. FIG. 5 is a diagram showing an example of generalizing the hierarchical structure of the cortical region. FIG. 6 is a diagram showing a generalized example of the hierarchical structure of the cortical region and the white matter region. FIG. 7 is a diagram showing an example of AFM according to the first embodiment. FIG. 8 is a diagram showing an example of AFM according to the first embodiment. FIG. 9 is a diagram showing an example of CFM according to the first embodiment. FIG. 10 is a diagram showing an example of PFM according to the first embodiment. FIG. 11 is a diagram showing an example of DSAM according to the first embodiment. FIG. 12 is a diagram showing another example of DSAM according to the first embodiment. FIG. 13 is a diagram showing an example of specifying the search start position by the specific function according to the first embodiment. FIG. 14 is a diagram showing another example of specifying the search start position by the specific function according to the first embodiment. FIG. 15 is a diagram showing an example of a matrix search by a specific function according to the first embodiment. FIG. 16 is a diagram showing an example of a matrix search by a specific function according to the first embodiment. FIG. 17 is a diagram showing an example of a matrix search by a specific function according to the first embodiment. FIG. 18 is a diagram showing an example of a matrix search by a specific function according to the first embodiment. FIG. 19 is a diagram showing an example of a matrix search by a specific function according to the first embodiment. FIG. 20 is a diagram showing an example of display of a matrix by the display control function according to the first embodiment. FIG. 21 is a diagram showing an example of detailed display of the matrix by the display control function according to the first embodiment. FIG. 22 is a diagram showing an example of displaying a matrix and an image by the display control function according to the first embodiment. FIG. 23 is a diagram showing an example of a three-dimensional image displayed by the display control function according to the first embodiment. FIG. 24 is a flowchart showing a flow of processing performed by the MRI apparatus according to the first embodiment. FIG. 25 is a diagram showing another example of displaying the matrix related to the display control function according to the first embodiment. FIG. 26 is a diagram showing another example of displaying the matrix related to the display control function according to the first embodiment. FIG. 27 is a diagram showing another example of displaying the matrix related to the display control function according to the first embodiment. FIG. 28 is a diagram showing another example of displaying a matrix and an image by the display control function according to the first embodiment. FIG. 29 is a diagram showing a configuration example of the MRI apparatus according to the second embodiment. FIG. 30 is a flowchart showing a flow of processing performed by the MRI apparatus according to the second embodiment. FIG. 31 is a diagram showing a configuration example of the image processing apparatus according to the third embodiment. FIG. 32 is a diagram showing a configuration example of the image processing apparatus according to the fourth embodiment.

(First Embodiment)
FIG. 1 is a diagram showing a configuration example of the MRI apparatus 100 according to the first embodiment. For example, as shown in FIG. 1, the MRI apparatus 100 includes a static magnetic field magnet 1, a gradient magnetic field coil 2, a gradient magnetic field power supply 3, a transmission coil 4, a transmission circuit 5, a reception coil 6, a reception circuit 7, a sleeper 8, and an input circuit. 9. The display 10, the storage circuit 11, and the processing circuits 12 to 15 are provided.

The static magnetic field magnet 1 is formed in a hollow substantially cylindrical shape (including a magnet having an elliptical cross section orthogonal to the central axis of the cylinder), and generates a uniform static magnetic field in the imaging space formed on the inner peripheral side. Let me. For example, the static magnetic field magnet 1 is realized by a permanent magnet, a superconducting magnet, or the like.

The gradient magnetic field coil 2 is formed in a hollow substantially cylindrical shape (including one having an elliptical cross section orthogonal to the central axis of the cylinder), and is arranged on the inner peripheral side of the static magnetic field magnet 1. The gradient magnetic field coil 2 has three coils that generate gradient magnetic fields along the x-axis, y-axis, and z-axis that are orthogonal to each other. Here, the x-axis, the y-axis, and the z-axis form a device coordinate system unique to the MRI device 100. For example, the x-axis direction is set to the vertical direction, and the y-axis direction is set to the horizontal direction. Further, the direction of the z-axis is set to be the same as the direction of the magnetic flux of the static magnetic field generated by the static magnetic field magnet 1.

The gradient magnetic field power supply 3 individually supplies a current to each of the three coils of the gradient magnetic field coil 2 to generate a gradient magnetic field along each of the x-axis, y-axis, and z-axis in the imaging space. By appropriately generating a gradient magnetic field along each of the x-axis, y-axis, and z-axis, it is possible to generate a gradient magnetic field along each of the lead-out direction, the phase encoding direction, and the slice direction that are orthogonal to each other. Here, the axes along the lead-out direction, the phase encoding direction, and the slice direction each form a logical coordinate system for defining the slice region or volume region to be imaged. In the following, the gradient magnetic field along the lead-out direction is referred to as a lead-out gradient magnetic field, the gradient magnetic field along the phase-encoded direction is referred to as a phase-encoded gradient magnetic field, and the gradient magnetic field along the slice direction is referred to as a slice gradient magnetic field. ..

Here, each gradient magnetic field is superimposed on the static magnetic field generated by the static magnetic field magnet 1 and is used to impart spatial position information to the magnetic resonance (MR) signal. Specifically, the lead-out gradient magnetic field imparts position information along the lead-out direction to the MR signal by changing the frequency of the MR signal according to the position in the lead-out direction. Further, the phase-encoded gradient magnetic field imparts position information in the phase-encoded direction to the MR signal by changing the phase of the MR signal along the phase-encoded direction. Further, the slice gradient magnetic field is used to determine the direction, thickness, position, and number of slice regions when the imaging region is the slice region, and the position in the slice direction when the imaging region is the volume region. By changing the phase of the MR signal according to the above, position information along the slice direction is given to the MR signal.

The transmission coil 4 is formed in a hollow substantially cylindrical shape (including one having an elliptical cross section orthogonal to the central axis of the cylinder), and is arranged inside the gradient magnetic field coil 2. The transmission coil 4 applies an RF (Radio Frequency) pulse output from the transmission circuit 5 to the imaging space.

The transmission circuit 5 outputs an RF pulse corresponding to the Larmor frequency to the transmission coil 4. For example, the transmission circuit 5 includes an oscillation circuit, a phase selection circuit, a frequency conversion circuit, an amplitude modulation circuit, and an RF amplifier circuit. The oscillator circuit generates RF pulses with a resonance frequency specific to the target nucleus placed in a static magnetic field. The phase selection circuit selects the phase of the RF pulse output from the oscillation circuit. The frequency conversion circuit converts the frequency of the RF pulse output from the phase selection circuit. The amplitude modulation circuit modulates the amplitude of the RF pulse output from the frequency conversion circuit according to, for example, a sinc function. The RF amplifier circuit amplifies the RF pulse output from the amplitude modulation circuit and outputs it to the transmission coil 4.

The receiving coil 6 is an RF coil that receives the MR signal emitted from the subject S. Specifically, the receiving coil 6 is attached to the subject S placed in the imaging space, and receives the MR signal emitted from the subject S under the influence of the RF magnetic field applied by the transmitting coil 4. Further, the receiving coil 6 outputs the received MR signal to the receiving circuit 7. For example, for the receiving coil 6, a dedicated coil is used for each part to be imaged. The dedicated coil referred to here is, for example, a receiving coil for the head, a receiving coil for the neck, a receiving coil for the shoulder, a receiving coil for the chest, a receiving coil for the abdomen, a receiving coil for the lower limbs, and a receiving coil for the spine. It is a receiving coil or the like.

The receiving circuit 7 generates MR signal data based on the MR signal output from the receiving coil 6, and outputs the generated MR signal data to the processing circuit 13. For example, the receiving circuit 7 includes a selection circuit, a pre-stage amplifier circuit, a phase detection circuit, and an analog-to-digital conversion circuit. The selection circuit selectively inputs the MR signal output from the receiving coil 6. The pre-stage amplifier circuit amplifies the MR signal output from the selection circuit. The phase detection circuit detects the phase of the MR signal output from the pre-stage amplifier circuit. The analog-to-digital conversion circuit generates MR signal data by converting the analog signal output from the phase detection circuit into a digital signal, and outputs the generated MR signal data to the processing circuit 13.

Here, an example in which the transmitting coil 4 applies an RF pulse and the receiving coil 6 receives an MR signal will be described, but the forms of the transmitting coil and the receiving coil are not limited to this. For example, the transmission coil 4 may further have a reception function for receiving an MR signal. Further, the receiving coil 6 may further have a transmitting function of applying an RF magnetic field. When the transmitting coil 4 has a receiving function, the receiving circuit 7 also generates MR signal data from the MR signal received by the transmitting coil 4. When the receiving coil 6 has a transmitting function, the transmitting circuit 5 also outputs an RF pulse to the receiving coil 6.

The sleeper 8 includes a top plate 8a on which the subject S is placed, and when the subject S is imaged, the top plate 8a enters the imaging space formed inside the static magnetic field magnet 1 and the gradient magnetic field coil 2. To insert. For example, the sleeper 8 is installed so that the longitudinal direction is parallel to the central axis of the static magnetic field magnet 1.

The input circuit 9 receives various instructions and various information input operations from the operator. Specifically, the input circuit 9 is connected to the processing circuit 15, converts the input operation received from the operator into an electric signal, and outputs the input operation to the processing circuit 15. For example, the input circuit 9 is realized by a trackball, a switch button, a mouse, a keyboard, a touch panel, and the like.

The display 10 displays various information and various images. Specifically, the display 10 is connected to the processing circuit 15, and converts various information and various image data sent from the processing circuit 15 into electrical signals for display and outputs the data. For example, the display 10 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, or the like.

The storage circuit 11 stores various data. Specifically, the storage circuit 11 stores MR signal data and image data for each subject S. For example, the storage circuit 11 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, a hard disk, an optical disk, or the like.

The processing circuit 12 has a sleeper control function 12a. Specifically, the sleeper control function 12a is connected to the sleeper 8 and outputs a control electric signal to the sleeper 8 to control the operation of the sleeper 8. For example, the sleeper control function 12a receives an instruction from the operator to move the top plate 8a in the longitudinal direction, the vertical direction, or the horizontal direction via the input circuit 9, and moves the top plate 8a according to the received instruction. The drive mechanism of the top plate 8a included in the sleeper 8 is operated. For example, the processing circuit 12 is realized by a processor.

The processing circuit 13 has an execution function 13a. Specifically, the execution function 13a executes various pulse sequences. Specifically, the execution function 13a executes various pulse sequences by driving the gradient magnetic field power supply 3, the transmission circuit 5, and the reception circuit 7 based on the sequence execution data output from the processing circuit 15. For example, the processing circuit 13 is realized by a processor.

Here, the sequence execution data is information that defines a pulse sequence indicating a procedure for collecting MR signal data. Specifically, the sequence execution data includes the timing at which the gradient magnetic field power supply 3 supplies the current to the gradient magnetic field coil 2, the strength of the supplied current, the strength of the RF pulse current supplied by the transmission circuit 5 to the transmission coil 4, and the strength of the RF pulse current. This is information that defines the supply timing, the detection timing at which the receiving circuit 7 detects the MR signal, and the like.

Further, the execution function 13a receives MR signal data from the reception circuit 7 as a result of executing various pulse sequences, and stores the received MR signal data in the storage circuit 11. The set of MR signal data received by the execution function 13a is arranged in two or three dimensions according to the position information given by the lead-out gradient magnetic field, the phase-encoded gradient magnetic field, and the slice gradient magnetic field described above. As a result, it is stored in the storage circuit 11 as data constituting the k space.

The processing circuit 14 has an image generation function 14a. For example, the processing circuit 14 is realized by a processor. The image generation function 14a generates an image based on the MR signal data stored in the storage circuit 11. Specifically, the image generation function 14a reads the MR signal data stored in the storage circuit 11 by the execution function 13a, and performs post-processing, that is, reconstruction processing such as Fourier transform on the read MR signal data to obtain an image. Generate. Further, the image generation function 14a stores the image data of the generated image in the storage circuit 11.

The processing circuit 15 controls the entire components of the MRI apparatus 100 by controlling each component of the MRI apparatus 100. For example, the processing circuit 15 is realized by a processor. For example, the processing circuit 15 receives inputs of various parameters related to the pulse sequence from the operator via the input circuit 9, and generates sequence execution data based on the received parameters. Then, the processing circuit 15 executes various pulse sequences by transmitting the generated sequence execution data to the processing circuit 13. Further, for example, the processing circuit 15 reads the image data of the image requested by the operator from the storage circuit 11, and outputs the read image to the display 10.

The configuration example of the MRI apparatus 100 according to the present embodiment has been described above. Under such a configuration, the MRI apparatus 100 according to the present embodiment is used for image analysis of a plurality of regions in the brain.

It should be noted that the plurality of regions in the brain referred to here are those in which the regions of the brain are divided by some intention. For example, the plurality of regions in the brain referred to here are functionally or anatomically divided regions of the brain. Hereinafter, an example in which a plurality of brain function localized regions (also referred to as localized regions) are used as the plurality of regions in the brain will be described, but the embodiment is not limited to this.

In recent years, in the field of neuroscience, elucidation of the brain function localized region and the connection relationship between the regions has been promoted by using the image captured by the MRI apparatus. For example, a method has been proposed in which the presence or absence of a disease is determined by analyzing the difference from a normal brain regarding a predetermined parameter such as the brightness value of an image for each region of brain function localization.

However, such a method has many elements from a research perspective and is not logically easy to understand from the perspective of clinical application. In particular, it can be said that the framework has not been shown in terms of diagnostic purposes, that is, application to clinical practice in line with definitive diagnosis and screening.

Therefore, there is a need for a framework that is easy for general neuroradiologists or physicians to understand and that uses information on the region of brain function localization and the connection between the regions according to the purpose of diagnosis.

In addition, it should be easy for general neuroradiologists or physicians to understand, and the diagnostic information should be presented in an easy-to-understand manner in a mechanism related to the framework using information on the region of brain function localization and the connection between the regions according to the purpose of diagnosis. Is required.

For this reason, the MRI apparatus 100 according to the present embodiment is configured to be able to support image analysis of a plurality of regions in the brain.

Specifically, in the present embodiment, the storage circuit 11 stores information representing a matrix showing a connection relationship between regions relating to a plurality of brain function localized regions. Here, before concretely explaining the matrix stored by the memory circuit 11, the hierarchical structure of the brain function localization region, the nerve fiber bundle, and the cortical region will be described.

FIG. 2 is a diagram for explaining a region of brain function localization. For example, as shown in FIG. 2, the brain region is divided into a plurality of brain function localized regions (regions separated by broken lines and solid lines shown in FIG. 2). Here, the lateralized brain function region includes a cortical region and a white matter region. The cortical area is the area of the cortex (also called gray matter) consisting of nerve cells and exists so as to cover the surface of the brain. The white matter region is a region of white matter (also called subcortical) that contains nerve fibers and exists inside the brain. Here, the smallest unit of the cortical region is called the gyrus. As a collection of gyrus, for example, there is a territory (Cortex) and the like, and there are units such as the frontal lobe, the occipital lobe, the temporal lobe, and the parietal lobe which are further divided into macros.

FIG. 3 is a diagram for explaining a nerve fiber bundle. A nerve fiber bundle is a collection of nerve fibers. Note that FIG. 3 shows intracranial nerve fibers connected to the region of brain function localization. Here, the nerve fiber (nerve) generally refers to an axon bundle, which is an aggregate of axons extending from a nerve cell. For example, as shown in FIG. 3, nerve fibers include an association fiber (AF) 21, a commissural fiber (CF) 22, and a projection fiber (PF) 23. The associative fiber 21 is a fiber connecting between brain function localized regions contained in the same hemisphere of the brain, and includes a short associative fiber and a long associative fiber. The commissural fiber 22 is a fiber that passes through the corpus callosum and connects between a brain function localization region included in the left hemisphere of the brain and a brain function localization region included in the right hemisphere. The projection fiber 23 is a fiber connecting the cortical region and the white matter region, and is divided into an efferent projection fiber from the cortical region to the white matter region and an afferent projection fiber from the white matter region to the cortical region. Be done. Here, the projection fiber 23 is a nerve fiber that connects the brain function localized region and the sensory organ (input) or the locomotor organ (output). A part of the projection fiber 23 is connected to the sensory organ and the locomotor organ via the thalamus and the internal capsule which are relay structures.

These nerve fibers basically pass through some small structures (small functional structures) in the white matter region, such as the thalamus and internal capsule described above. Therefore, in the following, the smallest unit of the brain function localization region including the cortical region and the region of some small structures (small functional structures) in the white matter region will be treated as a gyrus.

FIG. 4 is a diagram for explaining the hierarchical structure of the cortical region. In general, the gyrus, which is the smallest unit of the cortical region, is divided into multiple categories from a functional or anatomical point of view. For example, as shown in FIG. 4, the types of globus pallidus, putamen, caudate nucleus, substantia nigra, substantia nigra dense part, nucleus accumbens, Meinert basal nucleus, Ammon horn, cingulate gyrus, hippocampus In the paratour, olfactory infield, amygdala, and cingulate gyrus, the globus pallidus and putamen are classified as the lens nucleus, and the Ammon angle and the cingulate gyrus are classified as the hippocampus. Furthermore, the globus pallidus to the Nucleus basalis are classified into the basal ganglia, and the Ammon angle to the cingulate gyrus are classified into the limbic system. In this way, the cortical region is represented by a hierarchical structure in which the types of gyrus are classified stepwise.

FIG. 5 is a diagram showing an example of generalizing the hierarchical structure of the cortical region. In FIG. 5, "g1" to "g20" each represent the type of gyrus. Further, "sub-region1" indicates a sub-classification of the gyrus, and "region1", "region2", and "region3" indicate a middle classification of the gyrus. In addition, "light hemisphere" and "left hemisphere" are major classifications of the gyrus, and "light hemisphere" indicates the right hemisphere of the brain and "left hemisphere" indicates the left hemisphere of the brain.

Note that FIG. 5 shows an example in which the gyrus in the cortical region is classified by a hierarchical structure, but as described above, the gyrus exists not only in the cortical region but also in the white matter region. Therefore, for example, the gyrus of both the cortical region and the white matter region may be classified by a hierarchical structure. In this case, the gyrus of the cortical and white matter regions is classified, for example, based on the hierarchical structure defined by Talairach et al. (1988). The hierarchical structure of Talairach is composed of five layers: hemisphere, lobes of the brain, gyrus, tissue, and cell type. Here, the hemisphere is the largest classification, and the classification is gradually smaller in the order of lobes, gyrus, tissue, cell type, and subcell type.

FIG. 6 is a diagram showing a generalized example of the hierarchical structure of the cortical region and the white matter region. For example, as shown in FIG. 6, the hierarchical structure of the gyrus included in the cortical region and the white matter region includes the hemisphere layer (hemisphere), the lobes layer (love), the gyrus layer (gyrus), and the tissue layer (tisse). , A sub-cell type layer (sub-cell type) in which the cell type is further subdivided is added to the five layers of the cell type layer (cell type).

In FIG. 6, "hemisphere 1" indicates the classification contained in the hemisphere of the brain. Moreover, "love1" and "love2" indicate the classification included in the lobes of the brain. Further, "gyrus1", "gyrus2", and "gyrus3" indicate the classification included in the gyrus. Moreover, "gray matter" and "white matter" are classifications included in the panniculus. Further, "cell type1" and "cell type2" indicate the classification included in the cell type layer. In addition, "g1" to "g14" represent the type of gyrus, respectively. As shown in FIG. 6, the types of gyrus represented by "g1" to "g14" do not necessarily have to be classified in the same hierarchy.

Here, the classification "gray matter" included in the panniculus represents a cortical (gray matter) region, and "white matter" represents a white matter region. That is, the hierarchical structure shown in FIG. 6 includes both a cortical region (gray matter) and a white matter region (white matter).

Here, an example in which the gyrus is classified according to the hierarchical structure defined by Talairach et al. (1988) has been described, but the embodiment is not limited to this. For example, gyrus may be classified based on various brain atlases defined by various companies or organizations. The generalized hierarchical structure described above can be appropriately changed by subdividing or aggregating according to the definition of gyrus classification in various brain atlases.

In the present embodiment, the memory circuit 11 is a disorder to be diagnosed by dividing the nerve fiber bundles connecting the above-mentioned brain function localization region and between the brain function localization region and the organ system according to the function. It stores information that represents a matrix that divides the cortical region and the white matter region as regions.

Specifically, the memory circuit 11 stores information representing a matrix representing the connection relationship between the cortical region and the white matter region for each of the above-mentioned three types of nerve fiber bundles. That is, the memory circuit 11 stores a matrix showing a connection relationship by associative fibers, a matrix showing a connection relationship by commissural fibers, and a matrix showing a connection relationship by projection fibers, respectively.

In the following, the matrix showing the connection relationship by the association fibers will be referred to as AFM (Association Fiber Matrix). Further, a matrix showing a connection relationship by commissural fibers is called a CFM (Commissural Fiber Matrix). Further, a matrix showing a connection relationship by projection fibers is called a PFM (Projection Fiber Matrix).

7 and 8 are diagrams showing an example of AFM according to the first embodiment. As mentioned above, associative fibers are fibers that connect between cortical regions within the same hemisphere of the brain. Therefore, AFMs are divided into those relating to the right hemisphere and those relating to the left hemisphere. FIG. 7 shows an example of AFM related to the right hemisphere, and FIG. 8 shows an example of AFM related to the left hemisphere. Since each AFM has the same configuration, the AFM related to the right hemisphere will be described here as an example.

For example, as shown in FIG. 7, the AFM arranges cells "g1" to "g20" indicating the gyrus, which is the smallest unit of the cortical region, along the horizontal axis and the vertical axis, respectively, and further, the gyrus on the horizontal axis. It is a matrix in which cells showing white matter regions including nerve fiber bundles connecting between the vertical axis and the gyrus are arranged two-dimensionally along each axis. Further, in the AFM, cells showing a hierarchical structure in which the types of gyrus are classified stepwise are arranged outside the cells showing the gyrus on each of the horizontal axis and the vertical axis.

Here, in the AFM shown in FIG. 7, the cells with the diagonal lines are the nerve fibers that connect the gyrus at the corresponding position on the horizontal axis and the gyrus at the corresponding position on the vertical axis. It shows the white matter region that contains the bundle. For example, in the information representing AFM, "1" is given to the cell of the white matter region containing the nerve fiber bundle connecting the gyruses at the corresponding positions, and the gyruses at the corresponding positions are given. A "0" is given to the cells in the white matter region that contain the nerve fiber bundles that are not connected. In addition, in FIG. 7, it is shown that the cell with the grid pattern corresponds to the same gyrus assigned to each of the horizontal axis and the vertical axis, and is treated as an invalid cell showing no connection relationship. ..

For example, cell 31 shown in FIG. 7 is a bundle of nerve fibers connecting between the gyrus indicated by the cell "g3" arranged on the horizontal axis and the gyrus indicated by the cell "g9" arranged on the vertical axis. It shows the white matter area to be included. In other words, the cell 31 shows that the gyrus indicated by the cell "g3" arranged on the horizontal axis and the gyrus indicated by the cell "g9" arranged on the vertical axis have a connection relationship. Similarly, cell 32 shown in FIG. 7 is a nerve fiber bundle connecting between the gyrus indicated by the cell "g3" arranged on the horizontal axis and the gyrus indicated by the cell "g10" arranged on the vertical axis. Indicates a white matter region that includes. In other words, the cell 32 shows that the gyrus indicated by the cell "g3" arranged on the horizontal axis and the gyrus indicated by the "g10" arranged on the vertical axis have a connection relationship. That is, in the example shown in FIG. 7, the gyrus indicated by the cell "g3" arranged on the horizontal axis is the gyrus indicated by the cell "g9" arranged on the vertical axis, and the gyrus indicated by the cell "g10". It is connected to both.

According to such an AFM, for example, by performing a search using any of the cells "g1" to "g20" arranged along the horizontal axis as the search start position, the gyrus indicated by the cell set as the search start position. It is possible to detect the white matter region and the gyrus that have a connection relationship with. That is, according to AFM, by performing a search with one of the cells of a plurality of gyruses as the search start position, the gyruses that are connected among the other gyruses contained in the same hemisphere and those. The white matter region containing the nerve fiber bundles connecting the gyrus can be detected.

FIG. 9 is a diagram showing an example of CFM according to the first embodiment. As mentioned above, commissural fibers are fibers that connect between the cortical region contained in the left hemisphere of the brain and the cortical region contained in the right hemisphere. Therefore, in CFM, for example, the horizontal axis is assigned the gyrus included in the left hemisphere, and the vertical axis is assigned the gyrus included in the right hemisphere.

For example, as shown in FIG. 9, the CFM arranges cells "g1" to "g20" indicating the gyrus included in the left hemisphere along the horizontal axis, and arranges the gyrus included in the right hemisphere along the vertical axis. The cells "g1" to "g20" are arranged, and the cells showing the white matter region containing the nerve fiber bundle connecting between the gyrus on the horizontal axis and the gyrus on the vertical axis are two-dimensionally arranged along each axis. It is a matrix arranged in. Further, in the CFM, cells showing a hierarchical structure in which the types of gyrus are classified stepwise are arranged outside the cells showing the gyrus on each of the horizontal axis and the vertical axis.

Here, in the CFM shown in FIG. 9, the cells with the diagonal line pattern are located at the corresponding positions on the horizontal axis and the corresponding positions on the vertical axis, similarly to the cells included in the AFM described above. It shows a white matter region that contains a bundle of nerve fibers that connects to a gyrus. For example, in the information representing CFM, "1" is given to the cell of the white matter region containing the nerve fiber bundle connecting the gyruses at the corresponding positions, and the gyruses at the corresponding positions are given. A "0" is given to the cells in the white matter region that contain the nerve fiber bundles that are not connected.

In the AFMs shown in FIGS. 7 and 8, cells corresponding to the same gyrus assigned to the horizontal axis and the vertical axis are treated as invalid cells showing no connection relationship. On the other hand, in the CFM shown in FIG. 9, since the vertical axis and the horizontal axis correspond to different hemispheres on the left and right, cells at the same position as the cells invalidated by AFM are also included in the left and right hemispheres. It will show the same kind of gyrus connection.

For example, cell 41 shown in FIG. 9 is between the gyrus of the left hemisphere indicated by the cell "g4" arranged on the horizontal axis and the gyrus of the right hemisphere indicated by the cell "g4" arranged on the vertical axis. It shows the white matter region that contains the connecting nerve fiber bundles. In other words, the cell 41 has a connection relationship between the gyrus of the left hemisphere indicated by the cell "g4" arranged on the horizontal axis and the gyrus of the right hemisphere indicated by the cell "g4" arranged on the vertical axis. Is shown. Similarly, cell 42 shown in FIG. 9 is between the gyrus of the left hemisphere indicated by the cell "g4" arranged on the horizontal axis and the gyrus of the right hemisphere indicated by the cell "g11" arranged on the vertical axis. It shows the white matter region that contains the nerve fiber bundles that connect to. In other words, in the cell 42, the gyrus of the left hemisphere indicated by the cell "g4" arranged on the horizontal axis and the gyrus of the right hemisphere indicated by the cell "g11" arranged on the vertical axis have a connection relationship. Is shown. That is, in the example shown in FIG. 9, the gyrus of the left hemisphere indicated by the cell "g4" arranged on the horizontal axis is the gyrus of the right hemisphere indicated by the cell "g4" arranged on the vertical axis, and the cell "g4". It is connected to both the gyrus of the right hemisphere indicated by "g11".

According to such a CFM, as in the case of AFM, for example, by performing a search using any of the cells "g1" to "g20" arranged along the horizontal axis as the search start position, the search start position is set. The white matter region and the gyrus that are connected to the gyrus indicated by the cell can be detected. That is, according to CFM, by performing a search using the cell of the gyrus included in one of the left and right hemispheres as the search start position, the brains having a connection relationship in the gyrus included in the other hemisphere. The gyrus and the white matter region containing the nerve fiber bundles connecting those gyruses can be detected.

FIG. 10 is a diagram showing an example of PFM according to the first embodiment. As mentioned above, projection fibers are fibers that connect between the cortical region and the white matter region. Therefore, PFM is divided into those related to the right hemisphere and those related to the left hemisphere. The upper part of FIG. 10 shows an example of PFM related to the right hemisphere, and the lower part of FIG. 10 shows an example of PFM related to the left hemisphere. Since each PFM has the same structure, the PFM relating to the left hemisphere will be described here as an example.

For example, as shown in FIG. 10, the PFM arranges cells "g1" to "g20" indicating the gyrus, which is the smallest unit of the cortical region, along the horizontal axis, and further, the gyrus and the organ system on the horizontal axis. It is a matrix in which cells showing a white matter region containing a bundle of nerve fibers connecting between the cells are arranged along the horizontal axis. The organ system referred to here is, for example, a locomotor system, a sensory organ system, or the like. That is, PFM corresponds to a list of each organ system associated with each gyrus. Since many clinical symptoms can be said to be some kind of disorder of each of these organs, it can be said that this PFM directly reflects the clinical symptoms. Further, in PFM, cells showing a hierarchical structure in which the types of gyrus are classified stepwise are arranged below the cells showing gyrus.

Further, as described above, the projection fibers are divided into efferent projection fibers from the cortical region to the white matter region and afferent projection fibers from the white matter region to the cortical region. Therefore, the cell showing the white matter region containing the nerve fiber bundle that connects the gyrus on the horizontal axis and the organ system is also given information indicating whether the nerve fiber bundle is efferent or afferent. Will be done.

Further, as described above, the projection fiber is a nerve fiber that connects the brain function localized region and the sensory organ (input) or the locomotor organ (output). Therefore, the cell indicating the white matter region containing the nerve fiber bundle connecting the gyrus on the horizontal axis and the organ system is also given information indicating the type of the organ system to which the nerve fiber bundle is connected.

Here, in the PFM shown in FIG. 10, the shaded cells indicate the white matter region containing the nerve fiber bundles connecting between the gyrus and the organ system at the corresponding positions on the horizontal axis. There is. For example, in the information representing PFM, a cell in the white matter region containing a nerve fiber bundle connecting the gyrus and the organ system at a corresponding position may be described as "efferent" if the nerve fiber bundle is efferent. "+1" is given, and in the case of afferent, "-1" is given. Further, for example, in the information representing PFM, "0" is given to the cell of the white matter region including the nerve fiber bundle that does not connect between the gyrus and the organ system at the corresponding position. In addition, cells in the white matter region connecting between the corresponding gyrus and the organ system are labeled with the type of organ system.

For example, cell 51 shown in FIG. 10 shows a white matter region containing a bundle of nerve fibers connecting between the gyrus and the organ system indicated by cell "g4" arranged on the horizontal axis. In other words, cell 51 shows that the gyrus and the organ system indicated by the cell "g4" arranged on the horizontal axis have a connection relationship.

For example, "sensor1" to "sensor8" shown in FIG. 10 indicate the types of sensory organs, and "muscle1" to "muscle6" indicate the types of locomotor organs, respectively. The types of sensory organs referred to here are, for example, eyes, ears, nose, tongue, skin and the like. The types of locomotorium include hand muscles, leg muscles, facial facial muscles, and the like. For example, in the example shown in FIG. 10, in the PFM relating to the right hemisphere, the gyrus corresponding to the cell "g6" is connected to the sensory organ indicated by "sensor1", and the gyrus corresponding to "g8" is "muscle1". Indicates that it is connected to the locomotor system indicated by. Further, in the example shown in FIG. 10, in the PFM relating to the left hemisphere, the gyrus corresponding to the cell "g4" is connected to the sensory organ indicated by "sensor5", and the gyrus corresponding to "g8" is "muscle4". Indicates that it is connected to the locomotor system indicated by.

According to such a PFM, for example, by performing a search using any of the cells "g1" to "g20" arranged along the horizontal axis as the search start position, the gyrus indicated by the cell set as the search start position. White matter regions and organ systems that are connected to can be detected. That is, according to PFM, by performing a search with one of the cells of a plurality of gyrus as the search start position, the organ system connected to the gyrus and the nerve fiber bundle connecting them are included. The white matter region can be detected. In addition, it is possible to detect whether the nerve fiber bundles contained in the detected white matter region are efferent or afferent.

Further, in the present embodiment, the memory circuit 11 stores information representing a matrix in which the diagnostic purpose and the degree of attention according to the disease are set in each of the plurality of brain function localized regions. In the following, such a matrix will be referred to as a DSAM (Disease Specific Attention Matrix).

Specifically, the storage circuit 11 stores information representing DSAM for each diagnostic purpose. The diagnostic purpose referred to here includes definitive diagnosis and screening. That is, the storage circuit 11 stores information representing a DSAM for definitive diagnosis and information representing a DSAM for screening.

Here, the storage circuit 11 stores information representing the DSAM for each disease for the DSAM for definitive diagnosis. The diseases referred to here are, for example, Alzheimer's disease and Parkinson's disease. In addition, the storage circuit 11 stores information representing the DSAM that is not limited to the disease for the DSAM for screening. That is, since it is required not to specify a disease for screening, a DSAM that integrates a plurality of diseases is set. As described above, the storage circuit 11 stores information representing DSAM having different contents for the definitive diagnosis and the screening.

FIG. 11 is a diagram showing an example of DSAM according to the first embodiment. For example, as shown in FIG. 11, DSAM has cells "g1" to "g20" indicating the gyrus, which is the smallest unit of the cortical region, and cells "f1" to "f1" indicating nerve fiber bundles, along the horizontal axis. It is a matrix in which "f8" is arranged, and cells of attention regarding each gyrus and each nerve fiber bundle on the horizontal axis are arranged above each gyrus and each nerve fiber bundle. Further, in DSAM, cells showing a hierarchical structure in which the types of gyrus are classified stepwise are arranged outside the cells showing gyrus.

Here, for example, in the degree of attention, the degree of frequent occurrence is set for each region of brain function localized, which is a frequent occurrence site in a disease. Specifically, in the DSAM for definitive diagnosis, the degree of prevalence of the brain function localized region, which is the predominant site in each disease, is set as the degree of attention for each disease. For example, the degree of attention is set to rank values "0" to "4" that reflect the P value obtained from past literature information and the like as follows. In this example, the larger the rank value, the higher the degree of frequent occurrence. That is, the rank value represents the likelihood of the frequent occurrence site as an abnormal candidate region.

Rank value "0": Not noticed Rank value "1": 0.050 ≤ P value <0.100: Reference level Rank value "2": 0.010 ≤ P value <0.050
Rank value "3": 0.001 ≤ P value <0.010
Rank value "4": 0.000 ≤ P value <0.001

Further, for example, -log (P) may be used as it is as the degree of attention. For example, if P = 10 ^-4 , -log (10 ^-4 ) = 4 is set as the degree of attention.

On the other hand, in the DSAM for screening, the same degree of attention is set for all cells of the gyrus and nerve fiber bundles. For example, in the DSAM for screening, a rank value "3" is set as the degree of attention in all cells of the gyrus and nerve fiber bundles. In the DSAM for screening, the degree of attention may be weighted for each cell of the gyrus and the nerve fiber bundle.

The configuration of DSAM is not limited to that shown in FIG. For example, the DSAM may be configured in the same manner as the AFM.

FIG. 12 is a diagram showing another example of DSAM according to the first embodiment. The example shown in FIG. 12 shows a DSAM related to the left hemisphere, and the cells arranged on the vertical axis are not shown. For example, as shown in FIG. 12, the DSAM may be configured in the same manner as the AFM. In this case, for example, as shown in FIG. 12, a white matter region including a nerve fiber bundle connecting between the gyrus at the corresponding position on the horizontal axis and the gyrus at the corresponding position on the vertical axis. In the cell indicating (the cell with the shaded pattern), "f1" to "f8" representing the nerve fiber bundle and the degree of attention for each nerve fiber bundle are set.

The AFM, CFM, PFM and DSAM stored by the storage circuit 11 have been described above. Here, the contents set in each matrix are set based on, for example, known documents and experimental results in advance. Then, the contents set in each matrix are updated by incorporating information from the progress of research and clinical case analysis in the field of brain science at regular or arbitrary timings based on the accumulated images of the subjects. Thereby, the accuracy can be improved.

In AFM, CFM, PFM and DSAM, the number of gyruses arranged on the horizontal axis and the vertical axis is not limited to those shown in FIGS. 7 to 12, and is appropriately determined according to the definition of the division of the brain function localized region. May be changed to.

Further, the values set in AFM, CFM, PFM and DSAM are not necessarily limited to those described above. For example, in AFM, CFM and PFM, the value given to the cell in the white matter region may be set stepwise between "0" and "1". In that case, the value set in the cell is determined, for example, according to the analysis value obtained as the analysis result of the image.

Further, for example, the degree of attention set in DSAM may be set based on a reference value other than the P value. That is, the degree of attention set in DSAM may be set based on other statistical values representing the likelihood of the frequent occurrence site as an abnormality candidate region.

Then, in the present embodiment, the processing circuit 15 divides the MR image of the subject into the brain function localized region, analyzes the image for each region, refers to the value and the DSAM, and determines the region of interest. Image analysis and diagnostic determination proceed by prioritizing and identifying the relevant cortex and white matter from AFM, CFM, and PFM.

Returning to the description of FIG. 1, in the present embodiment, the processing circuit 15 has a setting function 15a, an analysis function 15b, a specific function 15c, a search function 15d, and a display control function 15e. The analysis function 15b is an example of an analysis unit within the scope of claims. Further, the specific function 15c is an example of a specific part within the scope of claims. Further, the search function 15d is an example of a search unit within the scope of claims. Further, the display control function 15e is an example of a display control unit within the scope of claims.

The setting function 15a receives the diagnosis purpose and the disease to be diagnosed from the operator. Specifically, the setting function 15a receives from the operator an operation of designating a diagnostic purpose and a disease to be diagnosed via the input circuit 9. At this time, the setting function 15a accepts an operation of designating either definitive diagnosis or screening for the purpose of diagnosis. Further, the setting function 15a further accepts an operation of designating a disease to be diagnosed when the purpose of diagnosis is a definitive diagnosis.

Then, when the diagnostic purpose and the disease are specified by the operator, the setting function 15a sets the imaging conditions for collecting the images of the analysis target determined in advance according to the diagnostic purpose and the disease. Further, the setting function 15a generates sequence execution data for collecting the image to be analyzed based on the set imaging conditions, and transmits the generated sequence execution data to the processing circuit 13. As a result, the execution function 13a of the processing circuit 13 collects MR signal data for generating an image to be analyzed. Further, the image generation function 14a of the processing circuit 14 generates an image to be analyzed based on the collected MR signal data.

The image to be analyzed here includes, for example, a morphological image such as a T1 weighted image, a T2 weighted image, a T2 * weighted image, and a FLAIR (Fluid Attenuation Inversion Recovery) image, and magnetization. Rate-weighted images, Quantitative Susceptibility Map (QSM), diffusion-weighted images, DTI (Diffusion Tensor Imaging) images, rs-fMRI (resting state functional MRI) images, or DTT (Diffusion Tensor Tractography) images, etc. Functional images are used.

For example, the storage circuit 11 stores in advance one or a plurality of protocols for collecting images used for analysis for each type of diagnosis purpose and diagnosis target. The protocol referred to here is information that defines the type of pulse sequence used for collecting the data that is the basis of the image to be collected, the values of various imaging parameters used for collecting the data, and the like. Then, when the diagnostic purpose and the disease are designated by the operator, the setting function 15a acquires one or a plurality of protocols corresponding to the designated diagnostic purpose and the disease by referring to the storage circuit 11, and acquires the protocol. Sequence execution data for collecting images used for analysis is generated based on the protocol. Thereby, the imaging conditions for collecting the images used for the analysis can be automatically set according to the diagnostic purpose and the disease.

In the image used for the analysis referred to here, in addition to the image to be analyzed described above, the brain region included in the image to be analyzed is divided into a plurality of brain function localized regions by the analysis function 15b described later. The form and functional image used when the processing is performed are included. For example, the morphological image referred to here includes an MP-RAGE (Magnetization Prepared Rapid Gradient Echo) image of the entire brain, and a functional image includes a QBI (Q-Ball Imaging) image of the entire brain. Further, the image used for the analysis includes an image displayed as a reference image by the display control function 15e described later. For example, the image displayed as the reference image referred to here includes a T2 weighted image of the entire brain and the like.

The analysis function 15b divides the brain region included in the image of the subject into a plurality of brain function localized regions, and performs texture analysis for each of the divided regions. For example, the analysis function 15b has a pixel brightness value, an FA (Fractional Anisotropy) value in a DTI image, a Mean Diffusivity (MD) value, an ADC (Apparent Diffusion Coefficient) value, and a magnetic susceptibility in a quantitative magnetic susceptibility map. Texture analysis is performed for parameters such as. The texture analysis referred to here is, for example, a basic statistical analysis of the average and variance of the luminance value as the first orderer, and an analysis of the degree of non-uniformity and a special pattern as the second orderer. The MP-RAGE image and T2 weighted image. Images, FA images, QSM, etc. are analyzed. In the MBs search by QSM, analysis is performed to automatically detect the inside of the region on a voxel basis. In addition, the analysis function 15b analyzes the difference from the normal brain regarding a predetermined parameter for each divided region.

At this time, the analysis function 15b may perform texture analysis for a plurality of types of parameters for each brain function localized region. Further, the analysis function 15b may perform volume calculation for each brain function localized region. Further, the analysis function 15b may analyze the difference from the normal brain for a plurality of types of parameters for each brain function localized region.

Specifically, the analysis function 15b analyzes the generated image when the image to be analyzed is generated by the image generation function 14a of the processing circuit 14.

First, the analysis function 15b divides the brain region included in the image to be analyzed into a plurality of brain function localized regions. At this time, for example, the analysis function 15b uses the morphological image captured as one of the analysis images to perform segmentation of the brain function localized region. For example, the analysis function 15b deploys a plurality of brain regions depicted in the morphological image by deforming and aligning a model in which a standard brain is divided into a plurality of brain function localized regions according to the morphological image. Segment to the area of. As the morphological image referred to here, for example, a T1-weighted image is used. Then, the analysis function 15b applies each brain function localization region segmented on the morphological image to other images to be analyzed, so that the brain region included in each image is applied to a plurality of brain function localization regions. Divide into.

Then, the analysis function 15b performs texture analysis for each gyrus, which is the smallest unit of the cortical region, and sets the degree of attention for each gyrus based on the analysis result. At this time, for example, the rank values "0" to "4" reflecting the P value are set for the attention level as in the DSAM.

Further, the analysis function 15b analyzes the difference from the normal brain regarding a predetermined parameter for each gyrus, which is the smallest unit of the cortical region, and sets the degree of attention for each gyrus based on the analysis result. At this time, for example, the rank values "0" to "4" reflecting the P value are set for the attention level as in the DSAM.

The specific function 15c starts searching for a matrix showing the connection relationship between the regions related to the plurality of regions in the brain based on the analysis result regarding the image of the subject and the degree of attention set for each of the plurality of regions in the brain. Identify the location.

For example, the specific function 15c shows the connection relationship between the regions related to the plurality of brain function localized regions based on the analysis result of the image of the subject and the degree of attention set for each of the plurality of brain function localized regions. Specify the search start position of the matrix. Specifically, the specific function 15c specifies the search start position of AFM, CFM, and PFM by using DSAM in which the diagnostic purpose and the degree of attention according to the disease are set for each of the plurality of brain function localized regions.

Here, the specific function 15c is designated by the operator among the information representing the definitive diagnosis DSAM stored by the storage circuit 11 when the diagnostic purpose received by the setting function 15a is a definitive diagnosis. Information representing the DSAM corresponding to the disease is used. On the other hand, when the diagnostic purpose received by the setting function 15a is screening, the specific function 15c uses the information representing the screening DSAM stored in the storage circuit 11.

Further, the specific function 15c specifies the search start position by using the analysis result of the texture analysis performed by the analysis function 15b. In addition, the specific function 15c further uses the analysis result of the analysis function 15b to analyze the difference from the normal brain regarding a predetermined parameter to specify the search start position. Here, the cell specified as the search start position by the specific function 15c indicates the gyrus or nerve fiber bundle that is a lesion candidate.

FIG. 13 is a diagram showing an example of specifying the search start position by the specific function 15c according to the first embodiment. For example, as shown in FIG. 13, the specific function 15c has a plurality of cells “g1” to “g20” and a plurality of cells “g1” to “g20” based on the DSAM 60, the analysis result 71 of the texture analysis, and the analysis result 72 based on the difference from the normal brain. From "f1" to "f8", the cell of the gyrus and the cell of the nerve fiber bundle as the search start position are specified. Note that FIG. 13 shows only the gyrus cell and the attention level cell for DSAM60. Further, FIG. 13 shows an example in which the gyrus indicated by the cell “g3” is selected as the search start position among the plurality of gyrus 73 indicated by the plurality of cells “g1” to “g20”.

For example, the specific function 15c specifies a cell having a attention level of "2" or more in at least one of the DSAM 60, the analysis result 71, and the analysis result 72 as the search start position. For example, in the example shown in FIG. 13, cells "g1" to "g5", "g9" to "g11", "g13", "g15", "g16", "g18" and "g19", and cells "f1" "To" f3 "are specified as search start positions. Here, when the DSAM for screening is used, when the same degree of attention is set for all the cells of the gyrus and the nerve fiber bundle, all the cells of the gyrus and the nerve fiber bundle are specified as the search start positions. Will be done.

Here, the specific function 15c specifies the cells of the gyrus and the nerve fiber bundle whose attention level is “2” or more as the search start position, but the threshold value of the attention level is not limited to this. For example, the specific function 15c may specify a cell of the gyrus and nerve fiber bundle whose attention level is greater than "0" as a search start position. Here, for example, the specific function 15c may change the threshold value of the degree of attention according to an instruction from the operator.

Further, here, the specific function 15c uses a cell whose attention level is equal to or higher than the threshold value in at least one of the DSAM 60, the analysis result 71 of the texture analysis, and the analysis result 72 due to the difference from the normal brain as the search start position. Although an example of the specific case has been described, the embodiment is not limited to this.

For example, the specific function 15c calculates the statistical value of the attention level set in the DSAM 60, the analysis result 71, and the analysis result 72 for each gyrus, and starts searching for a cell in which the calculated statistical value is equal to or higher than a predetermined threshold value. It may be specified as a position. For example, the specific function 15c calculates an average value, an addition value, a multiplication value, and the like as statistical values. Further, for example, the specific function 15c performs weighting addition in which the attention degrees of the DSAM 60, the analysis result 71, and the analysis result 72 are weighted at a predetermined ratio, and the added value of the weighted attention degree is used as a statistical value. It may be calculated.

Further, for example, the specific function 15c does not specify the search start position by using all of the DSAM 60, the analysis result 71, and the analysis result 72, but specifies the search start position by using any one or two of them. You may. In this case, for example, the specific function 15c obtains one or two of the DSAM 60, the analysis result 71 of the texture analysis, and the analysis result 72 due to the difference from the normal brain via the input circuit 9. The operation to be selected is accepted, and the search start position is specified based on the degree of attention of the one selected by the operator.

Further, for example, the specific function 15c does not specify the search start position using the DSAM 60, the analysis result 71, and the analysis result 72, but specifies the cell corresponding to the gyrus specified by the operator as the search start position. You may.

Further, for example, the specific function 15c specifies the search start position based on the analysis result of a plurality of types of parameters obtained by texture analysis and volume calculation and the analysis result of the difference between the normal brain and the plurality of types of parameters. You may.

FIG. 14 is a diagram showing another example of specifying the search start position by the specific function 15c according to the first embodiment. For example, as shown in FIG. 14, the specific function 15c has a plurality of cells "g1" to "g1" based on the DSAM 160, the analysis result 171 of the texture analysis and the volume calculation, and the analysis result 172 due to the difference from the normal brain. From "g14", the cell of the gyrus and the cell of the nerve fiber bundle to be the search start position are specified. Note that FIG. 14 shows an example in which the DSAM 160 includes cells “g1” to “g14” representing the gyrus. Further, FIG. 14 shows an example in which the analysis result 171 of the texture analysis and the volume calculation and the analysis result 172 due to the difference from the normal brain include the analysis results relating to a plurality of types of parameters (feature 1 to feature n), respectively. There is.

For example, the specific function 15c calculates the average value of the degree of attention for each gyrus for a plurality of types of parameters included in the analysis result 171. Then, the specific function 15c identifies a cell of the gyrus in which the calculated average value of the degree of attention exceeds a predetermined threshold value. For example, in the example shown in FIG. 1 4, when the average value of the attention regarding gyrus cell "g4" exceeds the threshold value, the specific features 15c may identify the cell "g4" as the candidate of the search start position To do.

After that, the specific function 15c refers to the DSAM 160, and for the cell specified as a candidate for the search start position, is the average value of the attention levels calculated from the analysis result 171 equal to or higher than the attention level set in the DSAM 160? Determine if not. Here, when the average value of the attention degree calculated from the analysis result 171 is equal to or higher than the attention degree set in the DSAM 160, the identification function 15c specifies the cell as the search start position. On the other hand, when the average value of the attention degree calculated from the analysis result 171 is lower than the attention degree set in the DSAM 160, the specific function 15c excludes the cell from the candidates for the search start position. For example, in the example shown in FIG. 1 4, for a cell "g4", when the average value of the attention degrees calculated from the analysis result 171 was set and attention "4" above DSAM160 a particular function The 15c specifies the cell "g4" as the search start position.

For example, the specific function 15c may specify the search start position by further using the analysis result 172 based on the difference from the normal brain in addition to the DSAM 160. In this case, for example, the specific function 15c calculates the average value of the degree of attention for each gyrus for a plurality of types of parameters included in the analysis result 172. Then, in the specific function 15c, the average value of the attention degree calculated from the analysis result 171 is equal to or higher than the attention degree set in the DSAM 160 for the cell specified as the candidate of the search start position, and from the analysis result 172. When it is equal to or more than the calculated average value of attention, the cell is specified as a candidate for the search start position. On the other hand, when the average value of the attention level calculated from the analysis result 171 is less than the attention level set in the DSAM 160 or less than the average value of the attention level calculated from the analysis result 172, the specific function 15c Excludes the cell from the candidates for the search start position.

Then, for example, as shown in FIG. 1 4, a particular feature 15c generates a list 173 that lists a plurality of cells included in DSAM160. Then, the specific function 15c sets the label "1" for the cell specified as the search start position in the generated list 173, and sets the label "0" for the cell not specified as the search start position.

In the example described here, an example in which the specific function 15c calculates the average value of the degree of attention for each gyrus for a plurality of types of parameters has been described, but the embodiment is not limited to this. For example, the specific function 15c may calculate the addition value of the degree of attention for each gyrus or the multiplication value for a plurality of types of parameters. Further, for example, the specific function 15c may calculate the addition value of the attention degree weighted for each parameter by performing weighting addition in which each parameter is weighted at a predetermined ratio for a plurality of types of parameters. .. In these cases, the DSAM 160 also sets the degree of attention based on the same criteria.

Returning to the description of FIG. 1, the search function 15d searches the matrix using the search start position specified by the specific function 15c. Specifically, the search function 15d refers to the information representing the AFM, CFM, and PFM stored in the storage circuit 11, and refers to the cells of the gyrus and nerve fiber bundles specified as the search start position by the specific function 15c. Use to explore each matrix.

15 to 19 are diagrams showing an example of a matrix search by the specific function 15c according to the first embodiment. Here, an example will be described in which the cell "g3" and the cells "f2" and "f4" are specified as search start positions by the search function 15d.

For example, as shown in FIG. 15, the search function 15d starts searching for the cell "g3" specified by the specific function 15c among the cells "g1" to "g20" arranged along the horizontal axis in the AFM30. Search as a position. Thus, for example, in the example shown in FIG. 15, in the same hemisphere of the brain, as the white matter region and the gyrus having a connection relationship with the gyrus indicated by the cell "g3", the white matter region indicated by the cells 33 and 34 and the cell " The gyrus indicated by "g9" and "g12" is obtained.

Further, for example, as shown in FIG. 16, the search function 15d selects the cell “g3” specified by the specific function 15c among the cells “g1” to “g20” arranged along the horizontal axis in the CFM40. The search is performed as the search start position. Thereby, for example, in the example shown in FIG. 16, in the hemisphere on the opposite side of the brain, the white matter region having a connection relationship with the gyrus shown by the cell "g3" and the white matter region shown by the cells 43 and 44 as the gyrus. The gyrus indicated by cells "g3" and "g9" is obtained.

Further, for example, as shown in FIG. 17, the search function 15d selects the cell “g3” specified by the specific function 15c among the cells “g1” to “g20” arranged along the horizontal axis in the PFM50. The search is performed as the search start position. Thereby, for example, in the example shown in FIG. 17, the white matter region and the organ system shown by the cell 52 are obtained as the white matter region and the organ system having a connection relationship with the gyrus shown by the cell “g3”. For example, the search function 15d detects the sensory organs indicated by "sensor2" as an organ system having a connection relationship with the gyrus indicated by cell "g3". At this time, by referring to the information given to the cell 52, it is also detected whether the nerve fiber bundle contained in the specified white matter region is efferent or afferent.

Further, for example, as shown on the left side of FIG. 18, the search function 15d has the cell “f2” specified by the specific function 15c among the cells “g1” to “g20” arranged along the horizontal axis in the AFM30. Is used as the search start position to perform the search. Thereby, for example, in the example shown in FIG. 18, cell 33 indicating the white matter region containing the nerve fiber bundle indicated by cell “f2” is specified. As a result, in the same hemisphere of the brain, as the gyrus connected by the nerve fiber bundle indicated by the cell "f2", the gyrus indicated by the cell "g3" arranged on the horizontal axis and the gyrus indicated by the cell "g3" were arranged on the vertical axis. The gyrus indicated by cell "g9" is obtained.

Similarly, for example, as shown on the right side of FIG. 18, the search function 15d is a cell “g1” to “g20” arranged along the horizontal axis in the CFM 40, and the cell “g1” to “g20” is specified by the specific function 15c. The search is performed with "f4" as the search start position. Thereby, for example, in the example shown in FIG. 18, cell 44 indicating the white matter region containing the nerve fiber bundle indicated by cell "f4" is identified. As a result, as the gyrus connected by the nerve fiber bundle indicated by the cell "f4", the gyrus of the left hemisphere indicated by the cell "g3" arranged on the horizontal axis and the cell "g9" arranged on the vertical axis. The gyrus of the right hemisphere indicated by "" is obtained.

The specific function 15c may further search the matrix by using the search result of each matrix. For example, as shown in FIG. 19, the search function 15d searches the AFM30 as shown in FIG. 15, and as a result, the cell “g9” indicates the gyrus having a connection relationship with the gyrus indicated by the cell “g3”. After the gyrus is obtained, the PFM50 is further searched with the searched cell "g9" as the search start position. Thereby, for example, in the example shown in FIG. 19, the white matter region and the organ system shown by the cell 53 are obtained as the white matter region and the organ system having a connection relationship with the gyrus shown by the cell “g9”. At this time, by referring to the information given to the cell 53, it is also detected whether the nerve fiber bundle contained in the specified white matter region is an efferent nerve fiber bundle or an afferent nerve fiber bundle. To.

Here, an example in which the cell "g3" and the cells "f2" and "f4" are specified as the search start positions by the search function 15d has been described, but the search function 15d is described by the specific function 15c. For all the identified gyrus and nerve fiber bundles, AFM, CFM and PFM are searched with each gyrus and each nerve fiber bundle as a search start position.

According to such a configuration, for patients with clinical symptoms or candidate patients who are asymptomatic but have some pathological changes, at the level of the gyrus, the region of brain function localization is impaired. Can be searched.

For example, with the main purpose of presenting lesion candidates in the light of clinical symptoms, "direct search" is used to search for the brain function localized region linked to the projection fibers connected to the sensory organs and locomotor organs that are directly linked to the clinical symptoms. It is possible to perform "indirect search" to search for a vulnerable brain function localization region connected to the lesioned brain function localization region (Epi-center).

To give an example, for example, as shown in FIG. 19, when the gyrus of the cell “g3” is specified as a lesion candidate by the specific function 15c, the search function 15d uses the cell “g3” as the search start position. By searching for PFM50, the organ system connected to the gyrus of cell "g3" can be identified. Searching for an organ system connected to the gyrus identified as a lesion candidate in this way is called "direct search".

On the other hand, when the search function 15d searches the AFM30, it can be seen that the gyrus of the cell "g3" is connected to the gyrus of the cell "g9" and the gyrus of the cell "g12". Here, for example, the search function 15d searches the PFM50 with the cell "g9" as the search start position, so that the organ system to which the gyrus of the cell "g9" is connected can be specified. Searching for an organ system connected to another gyrus connected to the gyrus identified as a lesion candidate in this way is called "indirect search".

According to recent studies, it is becoming clear that when an abnormality occurs in a certain gyrus, an abnormality also occurs in another gyrus that is connected to the gyrus.

In this regard, according to the above configuration, for example, as in the example shown in FIG. 19, even when the gyrus of cell “g9” is not identified as a lesion candidate due to low attention, the brain of cell “g3” By performing an indirect search for the gyrus, the organ system connected to the gyrus of cell "g9" is identified. Therefore, in an actual patient, if it is confirmed that clinical symptoms appear in the organ system identified by indirect search from the gyrus of cell "g3", the influence of the gyrus of cell "g3" causes cell "g9". It can be presumed that an abnormality has also occurred in the gyrus. As a result, it is possible to prevent the abnormality of the gyrus of the cell "g9" from being overlooked.

Returning to the description of FIG. 1, the display control function 15e controls so that the search result of the matrix by the search function 15d is displayed.

For example, the display control function 15e displays the search result of the matrix by the search function 15d on the display 10.

In the present embodiment, the display control function 15e controls so that the matrix showing the connection relationship between the regions relating to the plurality of regions in the brain is displayed as the search result of the matrix. Here, the display control function 15e narrows down a plurality of regions arranged along the first axis in the matrix to a part of the regions based on the degree of attention set for each of the plurality of regions in the brain. Control to be displayed.

For example, the display control function 15e displays on the display 10 a matrix showing the connection relationship between the regions relating to the plurality of brain function localized regions as the search result of the matrix. Here, the display control function 15e uses a plurality of brain function localized regions arranged along the horizontal axis in the matrix based on the degree of attention set for each of the plurality of brain function localized regions. Display by narrowing down to the localized area.

Specifically, the display control function 15e arranges information indicating a part of the brain function localization region along the horizontal axis, and arranges information indicating a part of the brain function localization region along the horizontal axis. A matrix in which information indicating a region of brain function localization having a connection relationship is arranged along the vertical axis is displayed on the display 10.

In the present embodiment, the display control function 15e is a brain specified as a search start position by the specific function 15c among the cells of a plurality of gyrus and nerve fiber bundles arranged along the horizontal axis in AFM, CFM or PFM. Display a matrix of cells of gyrus and nerve fiber bundles arranged along the horizontal axis. At this time, the display control function 15e arranges the cells of the white matter region and the gyrus obtained by the search by the search function 15d along the vertical axis.

That is, the display control function 15e narrows down and displays the contents of AFM, CFM, or PFM along the horizontal axis and the vertical axis, respectively. Although an example of displaying AFM will be described here, it is possible to display a matrix with narrowed contents by the same procedure when displaying CFM and PFM.

FIG. 20 is a diagram showing an example of a matrix display by the display control function 15e according to the first embodiment. In FIG. 20, an example of DSAM is shown in the lower left, and an example of AFM is shown in the upper left. Further, FIG. 20 shows an example in which cells “g4”, “g8” to “g11” and “g16” to “g19” are respectively specified as search start positions by the specific function 15c. Further, FIG. 20 shows an example in which cells “g11”, “g13”, “g14” and “g16” to “g20” along the vertical axis are obtained as search results by the search function 15d. ..

In this case, the display control function 15e arranges the cells "g4", "g8" to "g11" and "g16" to "g19" along the horizontal axis, for example, as shown in the upper right of FIG. The cells "g11", "g13", "g14" and "g16" to "g20" are arranged along the axis, and the matrix 80 in which the cells in the white matter region corresponding to each are arranged is displayed on the display 10.

At this time, the display control function 15e provides information indicating the brain function localization region and information indicating the functional or anatomical classification to which the brain function localization region belongs along the horizontal axis and the vertical axis. Display hierarchically.

FIG. 21 is a diagram showing an example of detailed display of the matrix by the display control function 15e according to the first embodiment. For example, as shown in FIG. 21, the display control function 15e displays a cell showing a hierarchical structure in which the types of gyrus are classified stepwise on each of the horizontal axis and the vertical axis, outside the cell showing the gyrus. .. For example, the display control function 15e has a cell of "sr1" indicating a subclassification of the gyrus, and "r1", "region2" and "region3" indicating a middle classification of the gyrus in order from the side closest to the cell of the gyrus. The cell, the cell of "left hemisphere" indicating the major classification of the gyrus is displayed.

At this time, the display control function 15e may display cells showing a hierarchical structure by abbreviation, for example, “r1” and “sr1” shown in FIG. Here, "r1" is an example of the abbreviation of "region1", and "sr1" is an example of the abbreviation of "sub-regioin1". Further, the display control function 15e may switch the display / non-display of the information indicating the hierarchical structure according to the instruction from the operator.

Further, the display control function 15e displays an image of the selected brain function localized region on the display 10 in response to the operation of selecting the brain function localized region displayed on the display 10. At this time, the display control function 15e enlarges the image of the brain function localized region and displays it on the display 10. Further, the display control function 15e is a brain function having a connection relationship between an image of a part of the brain function localized region arranged along the horizontal axis in the matrix 80 and the part of the brain function localized region. The image of the localized region is displayed side by side on the display 10.

By displaying a matrix with narrowed contents in this way, it is possible to make it easier to understand the region of brain function that has a high degree of attention. For example, in the case of Alzheimer's disease, by displaying the AFM in which the neural circuits representing the memory function and the emotional function are narrowed down, the clinical symptom and the brain function localized region can be easily associated with each other.

FIG. 22 is a diagram showing an example of displaying a matrix and an image by the display control function 15e according to the first embodiment. For example, as shown in FIG. 22, the display control function 15e displays the narrowed-down matrix 80 on the display 10. Although not shown in FIG. 22, cells showing a hierarchical structure in which the types of gyrus are classified stepwise are further added and displayed in the narrowed matrix 80 as shown in FIG. 21. Will be done.

Here, the display control function 15e displays an image of the entire brain as a reference image. For example, the display control function 15e includes an MP-RAGE (Magnetization Prepared Rapid Gradient Echo) image 91 for the entire brain, a QBI (Q-Ball Imaging) image 92 for the entire brain, and a T2 weighted image 93 for the entire brain. And are displayed. The image displayed as the reference image is not limited to the MP-RAGE image, the QBI image, and the T2W image. For example, the display control function 15e may display an image designated by the operator as a reference image.

Then, the display control function 15e receives from the operator an operation of selecting a cell of the gyrus or a cell of the white matter region included in the matrix 80 via the input circuit 9. Then, when the cells of the gyrus arranged on the horizontal axis are selected in the matrix 80, the display control function 15e enlarges and displays the image of the selected gyrus. For example, as shown in FIG. 22, when the cell “g8” arranged on the horizontal axis is selected in the matrix 80, the display control function 15e displays the MPR image 94 of the gyrus indicated by the cell “g8”. Enlarge and display.

Further, the display control function 15e displays the image of the selected gyrus, and at the same time, enlarges and displays the image of the white matter region connected to the gyrus indicated by the selected cell. For example, as shown in FIG. 22, the display control function 15e is connected to two gyruses indicated by the cell "g8" when the cell "g8" arranged on the horizontal axis is selected in the matrix 80. For the white matter region, DTT images 95 and 96 of the nerve fiber bundles contained therein are displayed.

Further, the display control function 15e displays the image of the selected gyrus, and at the same time, enlarges and displays the image of the gyrus connected to the selected gyrus. For example, as shown in FIG. 22, the display control function 15e is connected to the gyrus indicated by the cell "g8" when the cell "g8" arranged on the horizontal axis is selected in the matrix 80. The MPR image 97 of the gyrus indicated by "g13" and the MPR image 98 of the gyrus indicated by the cell "g14" are enlarged and displayed.

In FIG. 22, the display control function 15e is, MP-RAGE image 91 of the whole brain, QB I image 92 of the whole brain, the whole brain T2W images 93, MPR images 94, DTT images 95 and 96, MPR images 97 and An example of displaying 98 side by side has been shown, but the image display method is not limited to this. For example, the display control function 15e may switch and display each image according to an instruction from the operator.

Further, the display control function 15e may display an image of a predetermined cross section of the axial cross section, the sagittal cross section, and the coronal cross section, which are three orthogonal cross sections, for the MPR images 94, 97, and 98. An image of the cross section specified by the operator may be displayed.

Further, the display control function 15e displays an image showing a plurality of brain function localization regions on the display 10, and selects a specific brain function localization region from the brain function localization regions displayed on the image. The selected brain function localization region is highlighted from among the plurality of brain function localization regions arranged in the matrix 80 according to the selection operation. For example, the display control function 15e displays an image of the selected gyrus and at the same time displays a three-dimensional image 99 showing a region of the gyrus connected to the selected gyrus.

FIG. 23 is a diagram showing an example of a three-dimensional image displayed by the display control function 15e according to the first embodiment. For example, as shown in FIG. 23, the display control function 15e displays a three-dimensional image 99 of the brain generated by volume rendering or the like. Then, the display control function 15e displays a region 99a indicating the gyrus "g13" connected to the selected gyrus "g8" and a region 99b indicating the gyrus "g14" on the three-dimensional image 99. To do. In addition, the display control function 15e displays a DTT image 99c showing a nerve fiber bundle included in the white matter region connected to the selected gyrus "g8" on the three-dimensional image 99.

Here, the display control function 15e receives from the operator an operation of selecting a specific region from the region of the gyrus displayed on the three-dimensional image 99 via the input circuit 9. Then, when the region of the gyrus is selected on the three-dimensional image 99, the display control function 15e highlights the cell of the gyrus corresponding to the selected region in the matrix 80.

Further, the display control function 15e receives from the operator an operation of selecting a specific nerve fiber bundle from the nerve fiber bundles displayed on the three-dimensional image 99 via the input circuit 9. Then, when the nerve fiber bundle is selected on the three-dimensional image 99, the display control function 15e highlights the cell in the white matter region corresponding to the selected nerve fiber bundle in the matrix 80.

The processing functions of the processing circuit 15 have been described above. Here, for example, each of the above-mentioned processing functions is stored in the storage circuit 11 in the form of a program that can be executed by a computer. The processing circuit 15 reads each program from the storage circuit 11 and executes each read program to realize a processing function corresponding to each program. In other words, the processing circuit 15 in the state where each program is read has each processing function shown in FIG.

Although FIG. 1 has described an example in which each processing function is realized by a single processing circuit 15, the embodiment is not limited to this. For example, the processing circuit 15 may be configured by combining a plurality of independent processors, and each processor may execute each program to realize each processing function. Further, each processing function of the processing circuit 15 may be appropriately distributed or integrated into a single or a plurality of processing circuits.

FIG. 24 is a flowchart showing the flow of processing performed by the MRI apparatus 100 according to the first embodiment. For example, as shown in FIG. 24, in the MRI apparatus 100 according to the present embodiment, the setting function 15a first receives the diagnosis purpose and the disease to be diagnosed from the operator (step S101). In addition, the setting function 15a sets imaging conditions for collecting images to be analyzed, which are determined in advance according to the purpose of diagnosis and the disease (step S102).

After that, the execution function 13a and the image generation function 14a execute imaging (step S103). Specifically, the execution function 13a collects MR signal data for generating an image to be analyzed based on the sequence execution data generated by the setting function 15a. In addition, the image generation function 14a generates an image to be analyzed based on the collected MR signal data.

Subsequently, the analysis function 15b displays the image to be analyzed generated by the image generation function 14a on the display 10 (step S104). This makes it possible for the operator to confirm that the generated image has no problem as an analysis target.

Here, the analysis function 15b is a setting function for resetting the imaging conditions when the operator receives an input to the effect that the image to be analyzed is not approved (steps S105, No) via the input circuit 9. Control 15a. At this time, for example, the setting function 15a accepts an operation for resetting the imaging conditions from the operator, and causes the execution function 13a and the image generation function 14a to re-execute the imaging based on the reset imaging conditions. Then, the analysis function 15b redisplays the captured image as an image to be analyzed on the display 10.

Then, when the analysis function 15b receives an input from the operator to approve the image to be analyzed via the input circuit 9 (steps S105, Yes), the analysis function 15b performs image analysis of the image to be analyzed. First, the analysis function 15b divides the brain region included in the image to be analyzed into a plurality of brain function localized regions (step S106). After that, the analysis function 15b performs texture analysis for each divided region, and further analyzes the difference from the normal brain regarding a predetermined parameter (step S107).

Subsequently, the identification function 15c specifies the search start position of the matrix (step S108). Specifically, the specific function 15c uses DSAM to specify the search start positions of AFM, CFM, and PFM. At this time, when the diagnostic purpose received from the operator is a definitive diagnosis, the specific function 15c specifies the search start position using the DSAM for the definitive diagnosis, and the diagnostic purpose received from the operator is In the case of screening, the search start position is specified using the DSAM for screening.

Subsequently, the search function 15d searches the matrix using the specified search start position (step S109). Specifically, the search function 15d searches for AFM, CFM, and PFM using the search start position specified by the specific function 15c.

Subsequently, the display control function 15e displays a matrix in which the brain function localized region is narrowed down (step S110). Then, when the cell of the matrix is selected by the operator (step S111, Yes), the display control function 15e displays the image corresponding to the selected cell on the display 10 (step S112).

As described above, in the present embodiment, after the likelihood of the abnormal candidate region is specified from the DSAM from the result of the image analysis, the nerve fiber bundle and the gyrus as the abnormal candidate region related to the abnormal candidate region are used. It is searched and the search result is displayed.

Of the steps described above, steps S101 and S102 are realized, for example, by the processing circuit 15 calling a predetermined program corresponding to the setting function 15a from the storage circuit 11 and executing the steps. Further, in step S103, for example, the processing circuit 13 calls and executes a predetermined program corresponding to the execution function 13a from the storage circuit 11, and the processing circuit 14 stores a predetermined program corresponding to the image generation function 14a. It is realized by calling from 11 and executing.

Further, steps S104 to S107 are realized, for example, by the processing circuit 15 calling a predetermined program corresponding to the analysis function 15b from the storage circuit 11 and executing the program. Further, step S108 is realized, for example, by the processing circuit 15 calling a predetermined program corresponding to the specific function 15c from the storage circuit 11 and executing the program. Further, step S109 is realized, for example, by the processing circuit 15 calling a predetermined program corresponding to the search function 15d from the storage circuit 11 and executing the program. Further, steps S110 to S112 are realized, for example, by the processing circuit 15 calling a predetermined program corresponding to the display control function 15e from the storage circuit 11 and executing the program.

As described above, according to the first embodiment, the cortical region and the white matter region are the damaged regions to be diagnosed by dividing the nerve fiber bundles, which are the connection routes between the brain function localized regions and the organ system, by function. A matrix (AFM, CFM, PFM) is created. Further, a matrix (DSAM) is created in which the predominant site of the lesion in the disease and the value representing the predominant degree of the lesion in the brain disease unit are set in the respective brain function localization regions of the cortical region and the white matter region. Here, DSAMs having different contents for definitive diagnosis and screening are created.

Then, after a matrix is set according to the purpose of diagnosis, the brain region included in the image of the subject is divided into a plurality of brain function localized regions, and the image for each region is analyzed. In addition, the analysis value obtained by the analysis and the DSAM are referred to, and the priority of the region to be noted is determined. In addition, related gyrus and white matter regions are identified from AFM, CFM, and PFM, and image analysis and diagnostic determination are advanced. In addition, images of the subject are accumulated, and the values related to the predominant site of DSAM are sequentially updated from the comparison with the normal brain tissue, and the accuracy is improved.

This makes it easy for a general neuroradiologist or an internist to provide a framework using information on a region of brain function localization and a connection between the regions according to the purpose of diagnosis. As a result, the diagnostic ability of a general neuroradiologist or a physician can be enhanced, and an abnormal site can be efficiently detected in units of a brain function localized region.

Further, in the first embodiment, a matrix in which the cortical region and the white matter region are divided as the damaged region to be diagnosed in the nerve fiber bundle divided by function is presented, and at that time, only the region having a high degree of attention in DSAM is presented. It is selected and displayed.

This makes it easy for general neuroradiologists or physicians to understand, and presents the diagnostic information in an easy-to-understand manner in a mechanism related to a framework that uses information on the region of brain function localization and the connection between the regions according to the purpose of diagnosis. Can be done. As a result, the diagnostic ability of a general neuroradiologist or an internist can be enhanced, and an abnormal site can be efficiently searched and interpreted in units of a brain function localized region.

Here, when performing image analysis in units of the brain function localized region, it is considered that it takes time for the image analysis if the brain function localized region is subdivided. On the other hand, the MRI apparatus 100 according to the first embodiment displays the analysis result using a matrix showing the connection relationship between the regions related to the brain function localized region prepared in advance, so that the analysis result is displayed in real time. Can be presented.

The real time referred to here is a real time in a practical sense. That is, the real time here means that the time from capturing an image to presenting the analysis result is very short, for example, when a patient is continuously imaged in a clinical setting, the subject takes an image. It means the time spent in the examination room later, and does not necessarily mean that the time is zero.

From the above, according to the first embodiment, it is possible to support image analysis for a plurality of regions in the brain.

In the first embodiment described above, the method of displaying the narrowed-down matrix 80 can be appropriately changed. For example, the display control function 15e may further display the analysis values of the images relating to each of the plurality of brain function localized regions on the matrix 80.

25 to 27 are diagrams showing another example of the display of the matrix 80 according to the display control function 15e according to the first embodiment. For example, as shown in FIG. 25, the display control function 15e assigns different colors to the analysis values obtained by the image analysis performed by the analysis function 15b according to the magnitude of the analysis values. Then, when displaying the matrix 80, the display control function 15e colors and displays each of the cell of the white matter region, which is an element of the matrix, and the cell of the gyrus, which is an axis element of the matrix, according to the analysis value.

In this case, the display control function 15e switches the analysis value displayed on the matrix 80 according to the type of the image to be analyzed. For example, the display control function 15e receives an operation of selecting the type of analysis value from the operator via the input circuit 9. Then, the display control function 15e switches the display of the matrix 80 based on the analysis result of the image corresponding to the analysis value selected by the operator.

Further, here, the cells in the white matter region and the cells in the gyrus are colored and displayed according to the analysis value, but the method for displaying the analysis value is not limited to this. For example, each cell may be displayed in a gray scale in which the density is changed according to the analysis value, or each cell may be displayed in a different pattern according to the analysis value.

Further, for example, the display control function 15e may further display the efferent or afferent information obtained from the PFM on the matrix 80. For example, as shown in FIG. 26, the display control function 15e further displays a cell 81 for displaying the type of efferent or afferent in the vicinity of the cell of the gyrus, which is an axial element of the matrix 80. Then, the display control function 15e sets the cell 81 in the vicinity of the gyrus connected to the efferent nerve fiber bundle and the cell 81 in the vicinity of the gyrus connected to the afferent nerve fiber bundle. Display in different colors. Alternatively, for example, as shown in FIG. 27, the display control function 15e has a gyrus cell frame connected to an efferent nerve fiber bundle and a gyrus cell connected to an afferent nerve fiber bundle. The cell frame may be displayed in a different color.

In addition, in the first embodiment described above, information indicating the organ system obtained from PFM may be further displayed. For example, the display control function 15e displays on the display 10 information indicating the organ system detected from the PFM by the search function 15d together with the narrowed-down matrix 80.

FIG. 28 is a diagram showing another example of displaying a matrix and an image by the display control function 15e according to the first embodiment. For example, as shown in FIG. 28, the display control function 15e displays information 191 and 192 indicating the organ system detected from the PFM by the search function 15d on the display 10 in addition to the matrix 80 and each image shown in FIG. indicate. In addition, only one of the information 191 and 192 indicating the organ system may be displayed.

For example, the display control function 15e displays text information indicating the type of sensory organ and the type of locomotor organ as information 191 indicating the organ system, such as "sensor2" and "muscle5". Further, for example, the display control function 15e displays a model image representing the body shape of the subject as information 192 indicating the organ system, and a portion corresponding to the organ system detected by the search function 15d on the model image is displayed. Display identifiable. For example, the display control function 15e displays a graphic 193 indicating the position of the portion on the model image, or displays a display mode (for example, a color, a pattern, etc.) of the portion 194 on the model image corresponding to the portion. It may be different from the display mode of the part.

(Second Embodiment)
In the first embodiment described above, an example in which the display control function 15e displays a matrix in which the brain function localized region is narrowed down as a search result of AFM, CFM, and PFM has been described. Is not limited to this. For example, the display control function 15e may display a matrix in which the brain function localized region is narrowed down based on a DSAM prepared in advance according to predetermined medical information. Hereinafter, an example of such a case will be described as a second embodiment.

FIG. 29 is a diagram showing a configuration example of the MRI apparatus 150 according to the second embodiment. For example, as shown in FIG. 29, the MRI apparatus 150 includes a static magnetic field magnet 1, a gradient magnetic field coil 2, a gradient magnetic field power supply 3, a transmission coil 4, a transmission circuit 5, a reception coil 6, a reception circuit 7, a sleeper 8, and an input circuit. 9. The display 10, the storage circuit 211, and the processing circuits 12 to 14 and 215 are provided.

In this embodiment, the configuration of the MRI apparatus 150 will be mainly described with respect to the configuration different from the configuration of the MRI apparatus 100 according to the first embodiment, and plays the same role as the constituent elements shown in FIG. The components will be designated by the same reference numerals and detailed description thereof will be omitted.

Under such a configuration, the MRI apparatus 150 according to the present embodiment is used for image analysis of the brain performed in units of the lateralized brain function regions.

In the present embodiment, the storage circuit 211 stores information representing a matrix showing the connection relationship between the regions relating to the plurality of brain function localized regions, similarly to the storage circuit 11 described in the first embodiment. Specifically, the storage circuit 211 stores information representing AFM, CFM, PFM, and DSAM, similarly to the storage circuit 11 described in the first embodiment.

In the present embodiment, the storage circuit 211 stores information representing a DSAM in which a degree of attention is set according to predetermined medical information in each of a plurality of brain function localized regions. The predetermined medical information referred to here is, for example, a neurological symptom, a diagnostic purpose, a disease, or the like. Further, the neurological symptom referred to here is a specific function of the brain such as emotion and memory.

Further, for example, the medical information may be a feedback circuit of the brain. For example, recent studies have examined the correlation of lateralized brain function regions where disorders can occur in diseases such as Alzheimer's disease and Parkinson's disease. Such a correlation is called the feedback circuit of the brain. Therefore, a DSAM in which the degree of attention is set according to the feedback circuit may be used.

That is, the MRI apparatus 150 according to the present embodiment is used not only for diagnostic purposes but also for research purposes and the like.

Further, in the present embodiment, the processing circuit 215 has a setting function 215a, an analysis function 215b, and a display control function 215e. The display control function 215e is an example of a display control unit within the scope of claims.

The setting function 215a receives medical information from the operator. Specifically, the setting function 215a receives an operation of designating medical information from the operator via the input circuit 9. For example, the setting function 215a accepts neurological symptoms, diagnostic purposes, diseases, types of feedback circuits, and the like as medical information.

Then, when the medical information is designated by the operator, the setting function 215a sets the imaging conditions for collecting the image to be analyzed, which is determined in advance according to the medical information. Further, the setting function 215a generates sequence execution data for collecting the image to be analyzed based on the set imaging conditions, and transmits the generated sequence execution data to the processing circuit 13. As a result, the execution function 13a of the processing circuit 13 collects MR signal data for generating an image to be analyzed. Further, the image generation function 14a of the processing circuit 14 generates an image to be analyzed based on the collected MR signal data.

The analysis function 215b divides the brain region included in the image of the subject into a plurality of brain function localized regions, and performs texture analysis for each of the divided regions. In addition, the analysis function 215b analyzes the difference from the normal brain regarding a predetermined parameter for each divided region. Since the processing performed by the analysis function 215b is the same as the processing performed by the analysis function 15b described in the first embodiment, detailed description thereof will be omitted here.

The display control function 215e uses a plurality of brain function localization regions arranged along the horizontal axis in a matrix based on the degree of attention set for each of the plurality of brain function localization regions. The display is narrowed down to. Specifically, the display control function 215e arranges information indicating a part of the brain function localization region along the horizontal axis, and arranges information indicating a part of the brain function localization region along the horizontal axis. A matrix in which information indicating a region of brain function localization having a connection relationship is arranged along the vertical axis is displayed on the display 10.

In the present embodiment, the display control function 215e displays a matrix in which a plurality of brain function localized regions are narrowed down based on the degree of attention set in the DSAM. Specifically, the display control function 215e selects gyruses having a degree of attention of "2" or more in DSAM among a plurality of gyruses and nerve fiber bundles arranged along the horizontal axis in AFM, CFM or PFM. Display the matrix arranged along the horizontal axis. At this time, the display control function 215e arranges the white matter region and the gyrus connected to the gyrus arranged along the horizontal axis along the vertical axis in the AFM, CFM or PFM.

As a result, for example, as shown in FIGS. 20 to 22, the narrowed matrix 80 is displayed. In this embodiment as well, the display control function 215e may further display the analysis values of the images relating to each of the plurality of brain function localized regions on the matrix 80, as shown in FIGS. 25 to 27.

FIG. 30 is a flowchart showing a flow of processing performed by the MRI apparatus 150 according to the second embodiment. For example, as shown in FIG. 30, in the MRI apparatus 100 according to the present embodiment, the setting function 215a first receives medical information from the operator (step S201). In addition, the setting function 215a sets imaging conditions for collecting images to be analyzed, which are determined in advance according to medical information (step S202).

After that, the execution function 13a and the image generation function 14a execute imaging (step S203). Specifically, the execution function 13a collects MR signal data for generating an image to be analyzed based on the sequence execution data generated by the setting function 15a. In addition, the image generation function 14a generates an image to be analyzed based on the collected MR signal data.

Subsequently, the analysis function 215b displays the image to be analyzed generated by the image generation function 14a on the display 10 (step S204). This makes it possible for the operator to confirm that the generated image has no problem as an analysis target.

Here, the analysis function 215b is a setting function for resetting the imaging conditions when the operator receives an input to the effect that the image to be analyzed is not approved (steps S205, No) via the input circuit 9. Controls 215a. At this time, for example, the setting function 215a accepts an operation of resetting the imaging conditions from the operator, and causes the execution function 13a and the image generation function 14a to re-execute the imaging based on the reset imaging conditions. Then, the analysis function 215b redisplays the captured image on the display 10 as an image to be analyzed.

Then, when the analysis function 215b receives an input from the operator to approve the image to be analyzed via the input circuit 9 (steps S205, Yes), the analysis function 215b performs image analysis of the image to be analyzed. First, the analysis function 215b divides the brain region included in the image to be analyzed into a plurality of brain function localized regions (step S206). After that, the analysis function 215b performs texture analysis for each divided region, and further analyzes the difference from the normal brain regarding a predetermined parameter (step S207).

Subsequently, the display control function 215e displays a matrix in which a plurality of brain function localized regions are narrowed down based on the degree of attention set in the DSAM (step S208). Then, when the cell of the matrix is selected by the operator (step S209, Yes), the display control function 215e displays the image corresponding to the selected cell on the display 10 (step S210).

Of the above-mentioned steps, steps S201 and S202 are realized, for example, by the processing circuit 215 calling a predetermined program corresponding to the setting function 215a from the storage circuit 211 and executing it. Further, in step S203, for example, the processing circuit 13 calls and executes a predetermined program corresponding to the execution function 13a from the storage circuit 211, and the processing circuit 14 stores a predetermined program corresponding to the image generation function 14a. It is realized by calling from 211 and executing.

Further, steps S204 to S207 are realized, for example, by the processing circuit 215 calling a predetermined program corresponding to the analysis function 215b from the storage circuit 211 and executing the program. Further, steps S208 to S210 are realized, for example, by the processing circuit 215 calling a predetermined program corresponding to the display control function 215e from the storage circuit 211 and executing the program.

As described above, in the second embodiment, a matrix narrowing down the brain function localization region is displayed by using DSAM prepared according to medical information such as neurological symptoms, diagnostic purposes, diseases, and types of feedback circuits. Will be done.

As a result, not only for diagnostic purposes, but also for research purposes, etc., the diagnostic information can be easily understood by the operator, and the diagnostic information can be provided in a mechanism related to the framework using information on the brain function localized region and the connection between the regions according to the purpose. It can be presented in an easy-to-understand manner. As a result, it becomes possible to efficiently search for and interpret an abnormal site in units of the brain function localized region.

Therefore, according to the second embodiment, it is possible to support image analysis for a plurality of regions in the brain.

Although the embodiment of the MRI apparatus has been described in the first and second embodiments described above, the embodiment is not limited to this. For example, the technique disclosed in the present application can also be applied to an image processing apparatus. Hereinafter, an example in which the technique described in the first embodiment is applied to the image processing apparatus will be described as a third embodiment, and a case where the technique described in the second embodiment is applied to the image processing apparatus will be described. An example will be described as a fourth embodiment.

(Third Embodiment)
FIG. 31 is a diagram showing a configuration example of the image processing device 300 according to the third embodiment. For example, as shown in FIG. 31, the image processing device 300 according to the present embodiment is connected to the MRI device 100 and the image storage device 200 via the network 400.

The MRI apparatus 100 collects image data of a subject by utilizing a magnetic resonance phenomenon. Specifically, the MRI apparatus 100 collects magnetic resonance data from a subject by executing various imaging sequences based on imaging conditions set by the operator. Then, the MRI apparatus 100 generates two-dimensional or three-dimensional image data by performing image processing such as Fourier transform processing on the collected magnetic resonance data.

The image storage device 200 stores image data collected by various diagnostic imaging devices. Specifically, the image storage device 200 acquires image data from the MRI device 100 via the network 400, and stores the acquired image data in a storage circuit provided inside or outside the device. For example, the image storage device 200 is realized by a computer device such as a server device.

The image processing device 300 processes image data collected by various diagnostic imaging devices. Specifically, the image processing device 300 acquires image data from the MRI device 100 or the image storage device 200 via the network 400, and stores the image data in a storage circuit provided inside or outside the device. Further, the image processing apparatus 300 performs various image processing on the acquired image data, and displays the image data before or after the image processing on a display or the like. For example, the image processing device 300 is realized by a computer device such as a workstation.

For example, as shown in FIG. 31, the image processing device 300 includes an I / F (interface) circuit 310, a storage circuit 320, an input circuit 330, a display 340, and a processing circuit 350.

The I / F circuit 310 controls transmission and communication of various data transmitted and received between the image processing device 300 and another device connected via the network 400. Specifically, the I / F circuit 310 is connected to the processing circuit 350, converts the image data output from the processing circuit 350 into a format conforming to a predetermined communication protocol, and causes the MRI device 100 or the image storage device 200. Send. Further, the I / F circuit 310 outputs the image data received from the MRI apparatus 100 or the image storage apparatus 200 to the processing circuit 350. For example, the I / F circuit 310 is realized by a network card, a network adapter, a NIC (Network Interface Controller), or the like.

The storage circuit 320 stores various data. Specifically, the storage circuit 320 is connected to the processing circuit 350, and stores the input image data in response to a command sent from the processing circuit 350, or stores the stored image data in the processing circuit 350. Output. For example, the storage circuit 320 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, a hard disk, an optical disk, or the like.

The input circuit 330 receives various instructions and various information input operations from the operator. Specifically, the input circuit 330 is connected to the processing circuit 350, converts the input operation received from the operator into an electric signal, and outputs the input operation to the processing circuit 350. For example, the input circuit 330 is realized by a trackball, a switch button, a mouse, a keyboard, a touch panel, and the like.

The display 340 displays various information and various images. Specifically, the display 340 is connected to the processing circuit 350 and displays an image in various formats based on the image data output from the processing circuit 350. For example, the display 340 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, or the like.

The processing circuit 350 controls each component of the image processing device 300 in response to an input operation received from the operator via the input circuit 330. Specifically, the processing circuit 350 stores the image data output from the I / F circuit 310 in the storage circuit 320. Further, the processing circuit 350 displays the image data read from the storage circuit 320 on the display 340. For example, the processing circuit 350 is realized by a processor.

Under such a configuration, the image processing device 300 according to the present embodiment is used, for example, for image analysis of the brain performed in units of the brain function localized regions.

In the present embodiment, the storage circuit 320 stores information representing a matrix showing the connection relationship between the regions relating to the plurality of brain function localization regions, similarly to the storage circuit 11 described in the first embodiment. Specifically, the storage circuit 320 stores information representing AFM, CFM, PFM, and DSAM, similarly to the storage circuit 11 described in the first embodiment.

Further, the processing circuit 350 includes a setting function 351, an analysis function 352, a specific function 353, a search function 354, and a display control function 355. The specific function 353 is an example of a specific part within the scope of claims. Further, the search function 354 is an example of a search unit within the scope of claims. Further, the display control function 355 is an example of a display control unit within the scope of claims.

The setting function 351 receives the diagnosis purpose and the disease to be diagnosed from the operator in the same manner as the setting function 15a described in the first embodiment.

The analysis function 352 has the same function as the analysis function 15b described in the first embodiment. However, in the first embodiment, the analysis function 15b performs image analysis using the image to be analyzed generated by the image generation function 14a, whereas the analysis function 352 according to the present embodiment is the network 400. An image to be analyzed is acquired from the MRI apparatus 100 or the image storage apparatus 200 via the image, and image analysis is performed.

The specific function 353 has the same function as the specific function 15c described in the first embodiment. The search function 354 has the same function as the search function 15d described in the first embodiment. The display control function 355 has the same function as the display control function 15e described in the first embodiment.

Further, in the present embodiment, the input circuit 330, the display 340, and the storage circuit 320 further have the functions of the input circuit 9, the display 10, and the storage circuit 11 described in the first embodiment.

The processing functions of the processing circuit 350 have been described above. Here, for example, each of the above-mentioned processing functions is stored in the storage circuit 320 in the form of a program that can be executed by a computer. The processing circuit 350 reads each program from the storage circuit 320 and executes each read program to realize a processing function corresponding to each program. In other words, the processing circuit 350 in the state where each program is read has each processing function shown in FIG. 31.

Although FIG. 31 has described an example in which each processing function is realized by a single processing circuit 350, the embodiment is not limited to this. For example, the processing circuit 350 may be configured by combining a plurality of independent processors, and each processor may execute each program to realize each processing function. Further, each processing function of the processing circuit 350 may be appropriately distributed or integrated into a single or a plurality of processing circuits.

According to such a configuration, the same effect as that of the first embodiment can be obtained. Therefore, according to the third embodiment, as in the first embodiment, it is possible to support image analysis regarding a plurality of regions in the brain.

(Fourth Embodiment)
FIG. 32 is a diagram showing a configuration example of the image processing device 500 according to the fourth embodiment. For example, as shown in FIG. 32, the image processing device 500 according to the present embodiment is connected to the MRI device 100 and the image storage device 200 via the network 400. Since the configurations of the MRI apparatus 100 and the image storage apparatus 200 are the same as those shown in FIG. 31, description thereof will be omitted here.

The image processing device 500 processes image data collected by various diagnostic imaging devices. Specifically, the image processing device 500 acquires image data from the MRI device 100 or the image storage device 200 via the network 400, and stores the image data in a storage circuit provided inside or outside the device. Further, the image processing device 500 performs various image processing on the acquired image data, and displays the image data before or after the image processing on a display or the like. For example, the image processing device 500 is realized by a computer device such as a workstation.

For example, as shown in FIG. 32, the image processing apparatus 500 includes an I / F (interface) circuit 310, a storage circuit 520, an input circuit 330, a display 340, and a processing circuit 550.

In the present embodiment, the configuration of the image processing apparatus 500 will be mainly described with respect to the configuration different from the configuration of the image processing apparatus 300 according to the third embodiment, and the same as the components shown in FIG. 31. The components that play a role are designated by the same reference numerals, and detailed description thereof will be omitted.

Under such a configuration, the image processing device 500 according to the present embodiment is used for image analysis of the brain performed in units of the brain function localized regions.

In the present embodiment, the storage circuit 520 stores information representing a matrix showing the connection relationship between the regions relating to the plurality of brain function localization regions, similarly to the storage circuit 211 described in the second embodiment. Specifically, the storage circuit 520 stores information representing AFM, CFM, PFM, and DSAM, similarly to the storage circuit 211 described in the second embodiment.

Further, the processing circuit 550 includes a setting function 551, an analysis function 552, and a display control function 555. The display control function 555 is an example of a display control unit within the scope of claims.

The setting function 551 receives medical information from the operator in the same manner as the setting function 215a described in the second embodiment.

The analysis function 552 has the same function as the analysis function 215b described in the second embodiment. However, in the second embodiment, the analysis function 215b performs image analysis using the image to be analyzed generated by the image generation function 14a, whereas the analysis function 552 according to the present embodiment is the network 400. An image to be analyzed is acquired from the MRI apparatus 100 or the image storage apparatus 200 via the image, and image analysis is performed.

The display control function 555 has the same function as the display control function 215e described in the second embodiment.

The processing functions of the processing circuit 550 have been described above. Here, for example, each of the above-mentioned processing functions is stored in the storage circuit 520 in the form of a program that can be executed by a computer. The processing circuit 550 reads each program from the storage circuit 520 and executes each read program to realize a processing function corresponding to each program. In other words, the processing circuit 550 in the state where each program is read out has each processing function shown in FIG. 32.

Although FIG. 32 has described an example in which each processing function is realized by a single processing circuit 550, the embodiment is not limited to this. For example, the processing circuit 550 may be configured by combining a plurality of independent processors, and each processor may execute each program to realize each processing function. Further, each processing function of the processing circuit 550 may be realized by being appropriately distributed or integrated into a single processing circuit or a plurality of processing circuits.

According to such a configuration, the same effect as that of the second embodiment can be obtained. Therefore, according to the fourth embodiment, as in the second embodiment, it is possible to support image analysis regarding a plurality of regions in the brain.

In the third and fourth embodiments described above, an example in which the image processing device displays a matrix or an image on a display provided in the own device has been described, but the embodiment is not limited to this. For example, the image processing device may output a matrix or an image to an image display device connected via the network 400.

In recent years, an image processing system may be constructed in the form of a thin client in which the client device used by the operator executes the minimum necessary processing and the server device executes most of the processing. .. For example, in such an image processing system, the server device may have the function of the image processing device described in the third or fourth embodiment, and the client device may display a matrix or an image.

The word "processor" used in each of the above-described embodiments is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an integrated circuit for a specific application (Application Specific Integrated Circuit: ASIC), or a programmable device. It means a circuit such as a logical device (for example, a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). To do. Here, instead of storing the program in the storage circuit, the program may be configured to be directly embedded in the circuit of the processor. In this case, the processor realizes the function by reading and executing the program embedded in the circuit. Further, each processor of the present embodiment is not limited to the case where each processor is configured as a single circuit, and a plurality of independent circuits may be combined to be configured as one processor to realize its function. Good.

According to at least one embodiment described above, it is possible to support image analysis for a plurality of regions in the brain.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention as well as the invention described in the claims and the equivalent scope thereof.

100 Magnetic Resonance Imaging (MRI) device 15 Processing circuit 15c Specific function 15d Search function 15e Display control function

Claims (11)

Based on the analysis result of the image of the subject and the degree of attention set for each of the plurality of regions in the brain, the search start position of the region on the matrix showing the connection relationship between the regions related to the plurality of regions is specified. With a specific part
A magnetic resonance imaging apparatus including a search unit for searching a region having a connection relationship with a region corresponding to the search start position on the matrix. A display control unit that controls so that the search result of the matrix is displayed is further provided.
The magnetic resonance imaging apparatus according to claim 1. An analysis unit is further provided which divides the brain region included in the image of the subject into the plurality of regions and performs texture analysis for each divided region.
The specific unit specifies the search start position by using the analysis result of the texture analysis.
The magnetic resonance imaging apparatus according to claim 1 or 2. The analysis unit further analyzes the difference from the normal brain regarding a predetermined parameter for each of the divided regions.
The specific unit further uses the analysis result of analyzing the difference from the normal brain regarding the predetermined parameter to specify the search start position.
The magnetic resonance imaging apparatus according to claim 3. The matrix shows the connection by associative fibers connecting cortical regions contained in the same hemisphere of the brain.
The magnetic resonance imaging apparatus according to any one of claims 1 to 4. The matrix shows the connection relationship by commissural fibers connecting the cortical region contained in the left hemisphere of the brain and the cortical region contained in the right hemisphere.
The magnetic resonance imaging apparatus according to any one of claims 1 to 4. The matrix shows the connection relationship by the projection fibers connecting the cortical region and the white matter region.
The magnetic resonance imaging apparatus according to any one of claims 1 to 4. The specific unit specifies the search start position by using a matrix in which the degree of attention according to the diagnostic purpose and the disease is set for each of the plurality of regions.
The magnetic resonance imaging apparatus according to any one of claims 1 to 7. The diagnostic objectives include definitive diagnosis and screening.
The magnetic resonance imaging apparatus according to claim 8. The degree of attention is set for each region of brain function that is a frequent site in the disease.
The magnetic resonance imaging apparatus according to claim 8 or 9. Based on the analysis result of the image of the subject and the degree of attention set for each of the plurality of regions in the brain, the search start position of the region on the matrix showing the connection relationship between the regions related to the plurality of regions is specified. With a specific part
An image processing apparatus including a search unit for searching a region having a connection relationship with a region corresponding to the search start position on the matrix.

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