JP3425799B2 - Activation area extraction method - Google Patents
Activation area extraction methodInfo
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- JP3425799B2 JP3425799B2 JP11246394A JP11246394A JP3425799B2 JP 3425799 B2 JP3425799 B2 JP 3425799B2 JP 11246394 A JP11246394 A JP 11246394A JP 11246394 A JP11246394 A JP 11246394A JP 3425799 B2 JP3425799 B2 JP 3425799B2
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- 230000004913 activation Effects 0.000 title claims description 18
- 238000000605 extraction Methods 0.000 title claims description 10
- 238000005314 correlation function Methods 0.000 claims description 37
- 230000008859 change Effects 0.000 claims description 22
- 230000006870 function Effects 0.000 claims description 17
- 230000003111 delayed effect Effects 0.000 claims description 8
- 230000003925 brain function Effects 0.000 claims description 7
- 230000005291 magnetic effect Effects 0.000 claims description 7
- 238000000034 method Methods 0.000 description 34
- 210000003462 vein Anatomy 0.000 description 24
- 230000001934 delay Effects 0.000 description 13
- 210000003710 cerebral cortex Anatomy 0.000 description 10
- 230000008569 process Effects 0.000 description 9
- 239000003814 drug Substances 0.000 description 5
- 238000003384 imaging method Methods 0.000 description 5
- 229940079593 drug Drugs 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- 210000004556 brain Anatomy 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000002595 magnetic resonance imaging Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 108010054147 Hemoglobins Proteins 0.000 description 1
- 102000001554 Hemoglobins Human genes 0.000 description 1
- 108010064719 Oxyhemoglobins Proteins 0.000 description 1
- 238000000692 Student's t-test Methods 0.000 description 1
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 239000002775 capsule Substances 0.000 description 1
- 230000002490 cerebral effect Effects 0.000 description 1
- 210000004298 cerebral vein Anatomy 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000002599 functional magnetic resonance imaging Methods 0.000 description 1
- 230000000004 hemodynamic effect Effects 0.000 description 1
- 210000000337 motor cortex Anatomy 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 230000000638 stimulation Effects 0.000 description 1
- 238000012353 t test Methods 0.000 description 1
Landscapes
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Description
【発明の詳細な説明】
【０００１】
【産業上の利用分野】本発明は磁気共鳴（ＭＲＩ）装置
等を用いて取得した時系列画像データから、脳機能など
の活性化領域を抽出する方法に関する。
【０００２】
【従来の技術】磁気共鳴を利用し脳機能を解析する技術
が開発されている。この技術では外部刺激に対する大脳
皮質の反応を計測する。一般的には、光，音などの刺激
を所定期間被験者に加えつつ、数十から数百枚の時系列
画像を撮影する。一連の画像から、与えた刺激と同期し
て信号強度が増加した領域（活性化領域）を抽出し、そ
の領域での信号変化を観察する。信号変化の原因は、血
液中の酸化ヘモグロビンと還元ヘモグロビンの比率の変
化が考えられている。
【０００３】このような活性化領域の抽出方法として、
刺激印加前後の差分画像を作成する方法，ｔ検定を用い
る方法，相関関数を用いる方法が挙げられる。活性化領
域の抽出を相互相関関数を用いて行っている例としては
マグネティック レゾナンスイン メディスン、３０
巻、１６１〜１７３頁、１９９３年（MagneticResonanc
e in Medicine，３０，１６１−１７３（１９９３)）が
挙げられる。しかし、これらの方法では、外部刺激によ
り大脳皮質内の毛細血管周辺部のみでなく、その下流に
ある静脈の信号強度の増加も活性化領域と一緒に抽出さ
れる。すなわち、脳機能解析で活性化領域として抽出し
た部位と、ＭＲアンギオで撮影した静脈の部位との一致
を例示し、活性化領域の見誤りの危険性について指摘し
ている文献（マグネティック レゾナンス イン メデ
ィスン、３０巻，３８７〜３９２頁、１９９３年（Magn
etic Resonance in Medicine，３０，３８７−３９
２)）もある。
【０００４】
【発明が解決しようとする課題】活性化領域の誤抽出を
防止する方法として、まず、大脳皮質と静脈を区別せず
に活性化領域を抽出し、その後、前述のＭＲアンギオ画
像を利用し、静脈に対応する領域を除去する方法が考え
られる。しかしアンギオ画像は撮影に長時間を要し、被
験者の負担を増大させるため、好ましくない。別の方法
は、活性化領域の抽出手順の中に静脈を除去する処理を
加える方法が考えられる。しかし、従来の抽出方法では
この大脳皮膜と静脈を分離する処理については全く考慮
されていなかった。
【０００５】本発明の目的は、脳機能解析を行う上で問
題となる、活性化領域の見誤りを防止する抽出方法を提
供することにある。
【０００６】
【課題を解決するための手段】上記目的は、以下の手段
により達成される。すなわち、まず理想的な信号変化を
する関数を仮定する。また実際の信号変化を表す関数
を、時系列画像の信号強度を用いて、画素ごとに決定す
る。次に仮定した理想的な関数と実際の信号変化の関数
とから相互相関関数を計算し、相互相関関数の値を最大
あるいは最小にする遅延時間を導出する。この遅延時間
の値に閾値を設け、閾値以上の、あるいは閾値以下の、
あるいは上記遅延時間が適正範囲外の画素は活性化領域
から除外する。
【０００７】
【作用】遅延時間の値に閾値を設けることで、大脳皮質
と静脈の分離が可能になり、静脈を関心領域から除外す
ることができる。これにより活性化領域の見誤りを防止
することが可能になる。
【０００８】
【実施例】以下、本発明の実施例を図面を参照し説明す
る。なお実施例中では、相互相関関数を単に相関関数と
記す。
【０００９】図４は、脳機能解析に使用する時系列画像
データの計測方法の一例を示す説明図である。時系列画
像データの計測期間中に、静止期間１と静止期間３と刺
激印加期間２とを設けている。相関関数φfg(τ)は数１
で表される。
【００１０】
【数１】
【００１１】ここで関数ｆ(ｔ)は、実際の信号変化を表
す関数で、時系列画像の信号強度を用いて、画素ごとに
決定される。またｇ(ｔ)は刺激の印加に伴う理想的な信
号変化を表す関数で、例えば、静止期間１と静止期間３
には−１、刺激印加期間２には１の値をとる。なおｔは
計測開始からの時間を表し、τは遅延時間を表す。相関
関数φfg(τ)はある時点ｔにおける関数ｇ(ｔ)とそれか
らτへだたった時点ｔ−τにおける関数ｆ(ｔ−τ)との
かかわりあいを示している。
【００１２】ところで本解析における信号変化とは、各
時系列画像の信号強度変化なので、数１を離散的なデー
タに対応して変形する必要がある。変形後の相関関数φ
fg(ｍ)を数２とする。
【００１３】
【数２】
【００１４】ここでｎは計測開始からの画像番号、ｍは
遅延枚数を表す。
【００１５】図３に示すように、相関関数φfg(ｍ)は遅
延枚数ｍに依存して離散的に変化する。本発明では遅延
枚数の値、あるいは相関関数の値に閾値を設け、活性化
領域の抽出に使用する。例えば、相関関数φfg(ｍ)を最
大にする遅延枚数ｍ０の値を導出し、ｍ０の値が閾値Ｍ
以上の領域を除去する。または遅延枚数の値に適正範囲
を設け、この範囲に含まれない領域を除去しても良い。
【００１６】このような処理で、大脳皮質と静脈を分離
できる理由は以下の通りである。大脳皮質とその下流に
ある静脈とで、刺激の印加から信号変化が生じるまでの
時間を比較すると、静脈の信号変化は大脳皮質に比べて
遅れて変化する。そのため、ｍ０の値が閾値Ｍ以上の領
域を除去することで、活性化領域から静脈を除去するこ
とができる。
【００１７】活性化領域の抽出にかかわる処理の一例を
図１のフローチャートに示す。処理１では、相関関数を
計算する領域の削減を行う。ここでの代表的な処理は背
景ノイズの除去があげられる。これは画素の信号強度に
閾値を設け、閾値以下の領域を相関関数の計算対象から
除去する。また刺激を印加しても信号強度が大きく変化
しない領域を除くことも処理時間の短縮に有効である。
例えば静止期間１の画像から静止期間平均画像を、刺激
印加期間２から刺激印加期間平均画像を作成し、両平均
画像の信号強度の値を比較し、信号変化が小さい領域を
計算対象から除去する。なお処理１は相関関数を計算す
る画素数を削減するために行う処理であり、必須のもの
ではない。
【００１８】処理２では、残った画素に関して数２の計
算を行い、相関関数φfg(ｍ)を最大にする遅延枚数ｍ０
の値を抽出する。
【００１９】処理３では計算結果を出力する。その際、
閾値Ｍを指定する事が必要になる。閾値Ｍの入力法に関
しては、計算の度に異なる値を入力しても良いし、所定
の値をデフォルトとして保持しておき、領域除去を自動
化しても良い。なお入力するＭの値は、時系列画像の撮
影間隔や撮影部位に応じて設定することが望ましい。こ
の出力は、計算を行った画素の座標と、相関関数φfg
(ｍ)を最大にする遅延枚数ｍ０の値であっても良い。更
に望ましい方法は、例えば、計算を行った画素の信号強
度の値を、相関関数φfg(ｍ)を最大にする遅延枚数ｍ０
の値とした遅延画像を作成する方法がある。遅延画像か
ら、計算を行った画素の位置と求められたｍ０の値が対
応付けられ、活性化領域を簡便に把握することが可能に
なる。
【００２０】以上、本発明の基本的な実施例について記
したが、本発明には下記の応用が考えられる。
【００２１】刺激の印加により信号強度が減少する領域
が出現することが知られており、活性化領域と信号減少
領域の分布画像から、脳内の血行動態を把握することが
できる。本発明では活性化領域の抽出のみならず、信号
減少領域を抽出し、分布画像を作成することも可能であ
る。
【００２２】信号減少領域の抽出は、例えば相関関数φ
fg(ｍ)を最小にする遅延枚数ｍ０の値を導出し、ｍ０の
値が閾値Ｍ以上の領域を除去する。信号減少領域を画像
化し、事前に求めた活性化領域を抽出した遅延画像と重
ね合わせることで、分布画像が得られる。
【００２３】さらに簡便に分布画像を得る方法は、相関
関数φfg(ｍ)の絶対値を最大にする遅延枚数ｍ０を導出
し、ｍ０の値が閾値Ｍ以上の領域を除去する方法があ
る。活性化領域と信号減少領域の区別は、例えば二つの
領域を色分けして画像表示すればよい。
【００２４】処理２において、相関関数φfg(ｍ)を計算
する画像の枚数を限定することができる。例えば、図５
(ａ)に示すように、刺激印加直前及び直後の画像を用い
て、信号の立ち上り部分の相関関数φfg(ｍ)を活性化領
域の抽出に使用することができる。同様に刺激停止直前
及び直後の画像を用いて、信号の立ち下がりの部分の相
関関数φfg(ｍ)を活性化領域の抽出に使用することがで
きる(図５(ｂ))。これらは個々に静脈の除去に使用でき
るが、信号の立ち上がりと立ち下がりを別々に評価した
両結果を組合せ、活性化領域の抽出に使用しても良い。
【００２５】処理３において出力画像の内容を変更し、
遅延枚数ｍ毎の相関関数φfg(ｍ)の値を画像化し出力し
てもよい。この処理の一例を図２に示す。処理２′で
は、入力されたｍの値を数２に代入し、相関関数φfg
(ｍ)の値を計算する。処理３′では、相関関数φfg(ｍ)
の値を各画素の信号強度の値とし、相関関数画像を出力
する。処理２′及び処理３′は遅延枚数ｍが所定の値に
なるまで繰り返して行われる。この出力方法は、撮影部
位や条件、及び被験者の変更等により、遅延枚数ｍの値
を再検討する際に使用すると特に有効である。
【００２６】以上、活性化領域から静脈を除去する方法
について記述したが、処理３の出力を変更し、活性化領
域の下流にある静脈を抽出することも可能である。この
場合は、相関関数φfg(ｍ)を最大にする遅延枚数ｍ０の
値が、処理３で指定した閾値Ｍ以上の値である画素を抽
出し、遅延画像を作成する。同様に処理３′で作成され
る相関関数画像からも、静脈を抽出することが可能であ
る。
【００２７】なお、これまでは相関関数を離散的に扱っ
たが、理想的な信号変化を表す離散的な関数と、時系列
画像から求めた信号変化を表す離散的な関数とに補間を
施し、連続的な関数として扱うこともできる。これは、
時系列画像の撮影間隔が長い場合に特に有効である。す
なわち、刺激印加から大脳皮質や静脈で信号変化が生じ
る時間は撮影間隔には依存しないが、遅延枚数の時間分
解能は時系列画像の撮影間隔に依存している。そのため
撮影間隔が長い場合、大脳皮質における信号変化と静脈
での信号変化の時間差が、遅延画像の差に枚数に反映さ
れなくなる。相関関数の計算に使用する関数に補間を施
すことで、大脳皮質と静脈の信号変化の時間差を推測す
ることができ、活性化領域から静脈を除去することが可
能になる。
【００２８】抽出された活性化領域と静脈を同一画像上
に表示すると、両者の位置関係を簡単に把握できる。こ
の場合は、遅延枚数ｍの値、あるいは相関関数φfg(ｍ)
の値を用いて、活性化領域と静脈を分離し、少なくとも
どちらか一方を色分けして表示するとよい。
【００２９】
【発明の効果】本発明によれば活性化領域を抽出する際
に問題となる静脈を除去することが可能になる。また活
性化領域の下流にある静脈を抽出することも可能であ
る。Description: BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for extracting an active region such as a brain function from time-series image data obtained by using a magnetic resonance (MRI) apparatus or the like. . [0002] Techniques for analyzing brain functions using magnetic resonance have been developed. This technique measures the response of the cerebral cortex to external stimuli. In general, several tens to several hundreds of time-series images are photographed while stimuli such as light and sound are applied to a subject for a predetermined period. From a series of images, a region (activation region) in which the signal intensity increases in synchronization with the applied stimulus is extracted, and a signal change in the region is observed. The cause of the signal change is considered to be a change in the ratio of oxyhemoglobin to reduced hemoglobin in blood. As a method for extracting such an activated area,
A method of creating a difference image before and after the stimulus is applied, a method using a t-test, and a method using a correlation function are exemplified. Examples of the extraction of the activation region using the cross-correlation function include magnetic resonance in medicine, 30
Volume, pp. 161-173, 1993 (MagneticResonanc)
e in Medicine, 30 , 161-173 (1993)). However, according to these methods, the external stimulus extracts not only the periphery of the capillaries in the cerebral cortex but also the increase in the signal intensity of the veins downstream thereof together with the activated region. That is, a document (Magnetic Resonance in Medicine) which exemplifies the coincidence between a region extracted as an activation region in brain function analysis and a vein region photographed by MR angio and points out the danger of misidentification of the activation region. 30, Vol. 387-392, 1993 (Magn
etic Resonance in Medicine, 30 , 387-39
2)). [0004] As a method of preventing the erroneous extraction of the activated region, first, the activated region is extracted without distinguishing the cerebral cortex and the vein, and then the MR angiographic image is obtained. A method of removing a region corresponding to a vein by using the vein can be considered. However, an angiographic image is not preferable because it takes a long time to photograph and increases the burden on the subject. As another method, a method of adding a process of removing a vein in the procedure of extracting an activated region can be considered. However, in the conventional extraction method, no consideration has been given to the process of separating the cerebral capsule and veins. [0005] It is an object of the present invention to provide an extraction method for preventing an activation region from being viewed incorrectly, which is a problem in performing a brain function analysis. The above object is achieved by the following means. That is, first, a function that makes an ideal signal change is assumed. Further, a function representing an actual signal change is determined for each pixel using the signal intensity of the time-series image. Next, a cross-correlation function is calculated from the assumed ideal function and the actual signal change function, and a delay time for maximizing or minimizing the value of the cross-correlation function is derived. A threshold value is provided for the value of the delay time, and is equal to or greater than the threshold value or equal to or less than the threshold value.
Alternatively, pixels whose delay time is out of the appropriate range are excluded from the active area. By providing a threshold value for the delay time, the cerebral cortex and the vein can be separated, and the vein can be excluded from the region of interest. As a result, it is possible to prevent the activation region from being viewed incorrectly. An embodiment of the present invention will be described below with reference to the drawings. In the embodiments, the cross-correlation function is simply referred to as a correlation function. FIG. 4 is an explanatory diagram showing an example of a method of measuring time-series image data used for brain function analysis. During the measurement period of the time-series image data, a still period 1, a still period 3, and a stimulus application period 2 are provided. The correlation function φfg (τ) is
It is represented by [0010] Here, the function f (t) is a function representing an actual signal change, and is determined for each pixel using the signal intensity of a time-series image. G (t) is a function representing an ideal signal change accompanying the application of a stimulus.
Takes a value of -1 and a value of 1 during a stimulus application period 2. Note that t represents time from the start of measurement, and τ represents delay time. The correlation function φfg (τ) indicates the relationship between the function g (t) at a certain time point t and the function f (t−τ) at a time point t−τ from the time point t−τ. By the way, the signal change in the present analysis is a change in the signal intensity of each time-series image, and therefore it is necessary to transform Equation 1 according to discrete data. Correlation function φ after deformation
Let fg (m) be Equation 2. [0013] Here, n represents the image number from the start of the measurement, and m represents the number of delayed images. As shown in FIG. 3, the correlation function φfg (m) varies discretely depending on the number m of delays. In the present invention, a threshold value is provided for the value of the number of delay sheets or the value of the correlation function, and is used for extracting the activation area. For example, the value of the number of delays m0 that maximizes the correlation function φfg (m) is derived, and the value of m0 is set to the threshold M
The above area is removed. Alternatively, an appropriate range may be set for the value of the number of delays, and a region not included in this range may be removed. The reason why the cerebral cortex and the vein can be separated by such processing is as follows. Comparing the time from the application of a stimulus to the occurrence of a signal change between the cerebral cortex and a vein downstream thereof, the signal change in the vein changes later than in the cerebral cortex. Therefore, the vein can be removed from the activated region by removing the region where the value of m0 is equal to or greater than the threshold value M. FIG. 1 is a flowchart showing an example of a process relating to extraction of an activated area. In process 1, the area for calculating the correlation function is reduced. Typical processing here is removal of background noise. In this method, a threshold value is set for the signal intensity of a pixel, and a region below the threshold value is removed from the calculation target of the correlation function. In addition, removing a region where the signal intensity does not significantly change even when a stimulus is applied is also effective in shortening the processing time.
For example, an average image during the static period is created from the image in the static period 1 and an average image during the stimulus application period from the stimulus application period 2, and the signal intensity values of both average images are compared. . Processing 1 is processing to reduce the number of pixels for calculating the correlation function, and is not essential. In the process 2, the number of delays m0 that maximizes the correlation function φfg (m) is calculated by calculating Equation 2 for the remaining pixels.
Extract the value of In processing 3, the calculation result is output. that time,
It is necessary to specify the threshold M. As for the method of inputting the threshold value M, a different value may be input each time the calculation is performed, or a predetermined value may be held as a default and the area removal may be automated. It is desirable that the value of M to be input is set according to the photographing interval of the time-series image and the photographed part. This output is calculated based on the coordinates of the calculated pixel and the correlation function φfg.
The value of the number of delays m0 that maximizes (m) may be used. A more desirable method is, for example, to set the value of the signal intensity of the calculated pixel to the number of delays m0 that maximizes the correlation function φfg (m).
There is a method of creating a delayed image having a value of. From the delayed image, the position of the calculated pixel is associated with the obtained value of m0, so that the activation area can be easily grasped. The basic embodiment of the present invention has been described above. The present invention can be applied to the following applications. It is known that a region where the signal intensity is reduced by the application of the stimulus appears, and the hemodynamics in the brain can be grasped from the distribution image of the activated region and the signal reduced region. In the present invention, it is possible not only to extract the activation area but also to extract the signal reduction area and create a distribution image. The extraction of the signal reduction region is performed, for example, by using the correlation function φ
The value of the number of delays m0 that minimizes fg (m) is derived, and the region where the value of m0 is equal to or larger than the threshold M is removed. A distribution image can be obtained by imaging the signal reduction region and superimposing the activation region obtained in advance with the extracted delay image. As a further simple method of obtaining a distribution image, there is a method of deriving the number of delays m0 that maximizes the absolute value of the correlation function φfg (m), and removing a region where the value of m0 is equal to or larger than the threshold M. The activation region and the signal reduction region can be distinguished by, for example, displaying the image by color-coding the two regions. In the process 2, the number of images for which the correlation function φfg (m) is calculated can be limited. For example, FIG.
As shown in (a), the correlation function φfg (m) at the rising edge of the signal can be used for extracting the activation region using the images immediately before and after the application of the stimulus. Similarly, the correlation function φfg (m) at the trailing edge of the signal can be used for extracting the activation region using the images immediately before and after the stop of the stimulus (FIG. 5B). These can be used individually for vein removal, but the results of separately evaluating the rise and fall of the signal may be combined and used for extracting the active region. In process 3, the content of the output image is changed,
The value of the correlation function φfg (m) for each delay number m may be imaged and output. FIG. 2 shows an example of this processing. In processing 2 ', the input value of m is substituted into Equation 2, and the correlation function φfg
Calculate the value of (m). In process 3 ', the correlation function φfg (m)
Is used as the value of the signal intensity of each pixel, and a correlation function image is output. Processing 2 'and processing 3' are repeatedly performed until the number m of delays reaches a predetermined value. This output method is particularly effective when used when reviewing the value of the number of delays m due to changes in the imaging region and conditions, the subject, and the like. The method of removing a vein from the activated area has been described above. However, it is also possible to change the output of the processing 3 and extract a vein downstream of the activated area. In this case, a pixel whose value of the number of delays m0 that maximizes the correlation function φfg (m) is equal to or greater than the threshold M specified in the process 3 is extracted, and a delayed image is created. Similarly, veins can be extracted from the correlation function image created in the process 3 '. Although the correlation function has been treated discretely so far, interpolation is performed on a discrete function representing an ideal signal change and a discrete function representing a signal change obtained from a time-series image. , Can also be treated as a continuous function. this is,
This is particularly effective when the shooting interval of the time-series image is long. That is, the time at which a signal change occurs in the cerebral cortex or vein from the application of the stimulus does not depend on the imaging interval, but the time resolution of the number of delayed images depends on the imaging interval of the time-series image. Therefore, when the imaging interval is long, the time difference between the signal change in the cerebral cortex and the signal change in the vein is not reflected in the number of delayed images in the number of images. By performing interpolation on the function used for calculating the correlation function, it is possible to estimate the time difference between the signal change between the cerebral cortex and the vein, and it is possible to remove the vein from the activated area. When the extracted activated region and vein are displayed on the same image, the positional relationship between them can be easily grasped. In this case, the value of the number of delays m or the correlation function φfg (m)
The activation region and the vein may be separated using the value of, and at least one of them may be displayed in different colors. According to the present invention, it becomes possible to remove a vein which becomes a problem when extracting an activated area. It is also possible to extract a vein downstream of the activation area.
【図面の簡単な説明】
【図１】本発明における活性化領域の抽出手順の一例を
示すフローチャート。
【図２】本発明における活性化領域の抽出手順の一例を
示すフローチャート。
【図３】相互相関関数と遅延枚数の関係を示すグラフ。
【図４】脳機能解析に使用する時系列データの計測方法
の一例を示す説明図。
【図５】相互相関関数の求め方についての説明図。
【符号の説明】
１…静止期間、２…刺激印加期間、３…静止期間。BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flowchart showing an example of an activation area extraction procedure according to the present invention. FIG. 2 is a flowchart showing an example of a procedure for extracting an active area according to the present invention. FIG. 3 is a graph showing the relationship between the cross-correlation function and the number of delays. FIG. 4 is an explanatory diagram showing an example of a method of measuring time-series data used for brain function analysis. FIG. 5 is an explanatory diagram of how to obtain a cross-correlation function. [Explanation of Signs] 1 ... stationary period, 2 ... stimulation application period, 3 ... stationary period.
フロントページの続き (56)参考文献 Ｐ．Ａ．Ｂａｎｄｅｔｔｉｎｉ，Ａ. Ｊｅｓｍａｎｏｗｉｃｚ，ｅｔ．ａ ｌ．，”Ｐｒｏｃｅｓｓｉｎｇ Ｓｔｒ ａｔｅｇｉｅｓ ｆｏｒ Ｔｉｍｅ−Ｃ ｏｕｒｓｅ Ｄａｔａ Ｓｅｔｓ ｉｎ Ｆｕｎｃｔｉｏｎａｌ ＭＲＩ ｏｆ ｔｈｅ Ｈｕｍａｎ Ｂｒａｉｎ”, Ｍａｇｎｅｔｉｃ Ｒｅｓｏｎａｎｃｅ ｉｎ Ｍｅｄｉｃｉｎｅ，1993年，Ｖ ｏｌ．30，Ｎｏ．２，ｐ161−ｐ173 板垣博幸，小野寺由香里，山本悦治, 小泉英明，「超高速ＭＲＩを用いたヒト 脳運動野の描出と課題」，電気学会マグ ネティックス研究会資料，日本，1993年 11月29日，ＭＡＧ−93−224〜237，ｐ 105−ｐ113 (58)調査した分野(Int.Cl.7，ＤＢ名) A61B 5/055 ＪＩＣＳＴファイル（ＪＯＩＳ)Continuation of front page (56) References A. Bandettini, A. Jesmanowicz, et. a l. , "Processing Strategies for Time-Course Data Sets in Functional MRI of the Human Brain", Magnetic Resonance in Medicine, 1993, Vol. 30, No. 2, p161-p173 Hiroyuki Itagaki, Yukari Onodera, Etsuji Yamamoto, Hideaki Koizumi, "Depiction of Human Brain Motor Cortex Using Ultra-high-speed MRI" MAG-93-224 to 237, p105-p113 (58) Fields investigated (Int. Cl. 7 , DB name) A61B 5/055 JICST file (JOIS)
Claims (1)
の時系列画像データから脳機能の活性化領域を抽出する
活性化領域抽出法において、撮影した前記時系列画像デ
ータから求めた信号変化を表す関数１を前記時系列画像
データの信号強度を用いて画素ごとに決定し、理想的な
信号変化を表す関数２を仮定し、前記関数１と前記関数
２から相互相関関数を計算し、前記相互相関関数の値を
最大、最小あるいは前記相互相関関数の絶対値を最大に
する画像の遅延枚数を導出し、導出した遅延枚数の値に
閾値あるいは適正範囲を設け、前記遅延枚数の値が前記
閾値以下あるいは前記適正範囲内の画素を活性化領域と
して抽出することを特徴とする活性化領域抽出法。 (57) [Claims] [Claim 1] A magnetic resonance apparatus including information on the presence or absence of a stimulus
Of brain function activation region from time-series image data
In the active area extraction method, the time series image data
Function 1 representing the signal change obtained from the data
Determined for each pixel using the signal strength of the data, the ideal
Assuming a function 2 representing a signal change, the function 1 and the function
Calculate the cross-correlation function from 2 and calculate the value of the cross-correlation function
Maximum, minimum or maximum value of the cross-correlation function
The number of delayed images to be derived
A threshold or an appropriate range is provided, and the value of the number of delayed
Pixels equal to or less than a threshold or within the appropriate range are defined as an active area.
An activated area extraction method, characterized in that the activated area is extracted.
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| JP11246394A JP3425799B2 (en) | 1994-05-26 | 1994-05-26 | Activation area extraction method |
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| JP3425799B2 true JP3425799B2 (en) | 2003-07-14 |
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Non-Patent Citations (2)
| Title |
|---|
| Ｐ．Ａ．Ｂａｎｄｅｔｔｉｎｉ，Ａ．Ｊｅｓｍａｎｏｗｉｃｚ，ｅｔ．ａｌ．，"Ｐｒｏｃｅｓｓｉｎｇ Ｓｔｒａｔｅｇｉｅｓ ｆｏｒ Ｔｉｍｅ−Ｃｏｕｒｓｅ Ｄａｔａ Ｓｅｔｓ ｉｎ Ｆｕｎｃｔｉｏｎａｌ ＭＲＩ ｏｆ ｔｈｅ Ｈｕｍａｎ Ｂｒａｉｎ"，Ｍａｇｎｅｔｉｃ Ｒｅｓｏｎａｎｃｅ ｉｎ Ｍｅｄｉｃｉｎｅ，１９９３年，Ｖｏｌ．３０，Ｎｏ．２，ｐ１６１−ｐ１７３ |
| 板垣博幸，小野寺由香里，山本悦治，小泉英明，「超高速ＭＲＩを用いたヒト脳運動野の描出と課題」，電気学会マグネティックス研究会資料，日本，１９９３年１１月２９日，ＭＡＧ−９３−２２４〜２３７，ｐ１０５−ｐ１１３ |
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