JP4509531B2 - Acute cerebral infarction diagnosis and treatment support device - Google Patents

Description

  The present invention relates to an acute cerebral infarction diagnosis and treatment support apparatus that provides information useful for diagnosis and treatment policy determination of acute cerebral infarction.

  In the medical field, diagnosis of acute cerebral infarction and determination of a treatment policy require high accuracy as well as rapidity. For example, even if an ischemic site is discovered, the immediate thrombolysis treatment involves the risk of bleeding, and therefore requires extreme caution. Diagnosis of acute cerebral infarction and determination of treatment policy require a wealth of knowledge and experience.

  An object of the present invention is to provide information useful for diagnosis of acute cerebral infarction and decision of a treatment policy.

An acute cerebral infarction diagnosis and treatment support apparatus according to an aspect of the present invention stores a CT image relating to a cross section of a subject's head and a cerebral blood flow image representing a spatial distribution of a cerebral blood flow dynamic index relating to substantially the same cross section of the subject. A storage unit, a contrast-enhanced image that emphasizes a region of a specific CT value range from the CT image, or an image generation unit that generates a sulcus-enhanced image that emphasizes a sulcus region from the CT image, and the contrast-enhanced image or An image processor that superimposes a first ROI relating to ischemia identified from the cerebral groove emphasis image and a second ROI relating to low blood flow identified from the cerebral blood flow image on the CT image; the first ROI and the first ROI; And a display unit for displaying the CT image on which 2ROI is superimposed.

  According to the present invention, it is useful for diagnosis and treatment policy determination of acute cerebral infarction to display two types of ROIs of an ischemic region and a low blood flow region superimposed on a CT image.

  Hereinafter, an acute cerebral infarction diagnosis and treatment support apparatus according to the present invention will be described with reference to the drawings. The acute cerebral infarction diagnosis and treatment support apparatus handles CT images (spatial distribution of CT values) acquired by an X-ray computed tomography apparatus. Here, the acute cerebral infarction diagnosis and treatment support apparatus will be described as an apparatus incorporated in an X-ray computed tomography apparatus. However, the acute cerebral infarction diagnosis and treatment support apparatus can be configured separately from the X-ray computed tomography apparatus.

  As shown in FIG. 1, the X-ray computed tomography apparatus of this embodiment includes a gantry 1 configured to collect projection data related to a subject. The gantry 1 includes an X-ray tube 10 and an X-ray detector 23. The X-ray tube 10 and the X-ray detector 23 are mounted on a ring-shaped rotating frame 12 that is rotationally driven by a gantry driving device 25. The central portion of the rotating frame 12 is opened, and the subject P placed on the top 2a of the bed 2 is inserted into the opening. The rotation center axis of the rotary frame 12 is defined as a Z axis (slice direction axis), and a plane orthogonal to the Z axis is defined as two orthogonal axes of XY.

  A tube voltage is applied between the cathode and anode of the X-ray tube 10 from the high voltage generator 21, and a filament current is supplied to the filament of the X-ray tube 10 from the high voltage generator 21. X-rays are generated by applying a tube voltage and supplying a filament current. As the X-ray detector 23, either a one-dimensional array detector or a two-dimensional array detector (also referred to as a multi-slice detector) may be employed. The X-ray detection element has a square light receiving surface of 0.5 mm × 0.5 mm, for example. For example, 916 X-ray detection elements are arranged in the channel direction. A two-dimensional array type detector has 40 rows arranged in the slice direction, for example. What consists of a single column is a one-dimensional array type detector.

  A data acquisition device 26 generally called a DAS (data acquisition system) converts a signal output from the detector 23 for each channel into a voltage signal, amplifies it, and converts it into a digital signal. This data (raw data) is supplied to the computer unit 3 outside the gantry. The pre-processing unit 34 of the computer unit 3 performs correction processing such as sensitivity correction on the data (raw data) output from the data collection device 26 and outputs projection data. This projection data is sent to and stored in the data storage device 37 of the computer system 3.

  The computer system 3 includes a system controller 29, a scan controller 30, a reconstruction processing unit 36, a display unit 38, an input device 39 including a mouse and a keyboard, a cerebral ischemia diagnosis support together with the preprocessing unit 34 and the storage device 37. An apparatus (also referred to as a cerebral ischemia analysis (CT-DE) apparatus) 40, a cerebral infarction expert system 50, and a CBP study processing unit 120 are configured.

  As will be described in detail later, the cerebral ischemia diagnosis support apparatus 40 processes, for example, the spatial distribution (CT image) of CT values reconstructed by the reconstruction processing unit 36 as information useful for cerebral ischemia diagnosis support. Thus, a contrast-enhanced image and a sulcus-enhanced image are generated. As illustrated in FIG. 2A, the contrast-enhanced image is an image in which the brain tissue region of the original CT image is color-coded into at least three types according to the CT value. The sulcus-enhanced image is an image in which the cerebrospinal fluid region of the original image is color-coded into at least two types according to the CT value. The ischemic region appears as a low CT value region. From the contrast-enhanced image and the sulcus-enhanced image, the low CT value region can be extracted as a region having a high possibility of ischemia.

  As will be described in detail later, the CBP study processing unit 120 captures the state of passage of the contrast agent by dynamic CT, and uses the continuous image as an index that quantitatively represents the blood flow dynamics of the capillaries in the brain tissue. Calculation is performed for each pixel of CBP, CBV, MTT, and Err, and a spatial map (brain blood flow image) of these indexes is generated. FIG. 2B illustrates a CBP map. CBP represents the unit volume in the capillary of the brain tissue and the blood flow volume per unit time [ml / 100 ml / min], CBP represents the blood volume per unit volume in the brain tissue [ml / 100 ml], and MTT represents The blood average transit time [seconds] of the capillary blood vessel is represented, and Err represents the sum of the residuals or the square root of the squared sum of the residuals when approximating the transfer function. It is possible to extract a low blood flow region as a region having a high possibility of ischemic cerebrovascular disorder from an index map (cerebral blood flow image) that quantitatively represents blood flow dynamics of capillaries in these brain tissues. it can.

  In this embodiment, two different types of images, that is, a low CT value region contrast-enhanced image obtained by performing threshold processing or clustering processing on a CT value space distribution (CT image) by non-contrast CT imaging, and the like, A map such as CBP (cerebral blood flow image) that quantitatively represents blood flow dynamics of capillaries in brain tissue obtained by processing continuous images by dynamic CT imaging is provided, and is identified from a contrast-enhanced image or the like A frame line (first ROI) indicating the contour of the low CT value region (ischemic region) and a frame line (second ROI) indicating the contour of the low blood flow region identified from the cerebral blood flow image in the CT image. The most important feature is that they are displayed superimposed. That is, the ischemic region viewed from a simple CT value does not match the ischemic region viewed from a quantitative value of blood flow dynamics of capillaries in brain tissue. The latter region, which is usually hypersensitive to sexual cerebrovascular disorders, is wider than the ischemic region viewed from the former simple CT value. The ischemic region that can be determined from the simple CT value of the former is often unrecoverable. In addition, ischemic treatment involves the risk of intracerebral hemorrhage. In order to determine ischemic treatment in such a risk, it is necessary to expect a certain degree of recovery possibility (therapeutic effect). A region having a difference between the low CT value region and the low blood flow region can be regarded as a region in which recovery potential remains. This is very useful information when making treatment decisions.

  FIG. 3 shows an operation procedure according to the present embodiment. This operation functions under the control of the cerebral infarction expert system 50. In the present embodiment, there are two types of CT imaging (scanning for data collection): simple CT imaging S1 and contrast dynamic CT imaging S5. Typically, simple CT imaging is performed for a specific slice of an axial related to the subject's head, and a contrast medium is injected into the subject at an arbitrary time after the completion of simple CT imaging, and dynamic CT imaging is performed for the same specific slice. Is started.

  Simple CT imaging refers to a scanning operation for collecting one set or several sets of projection data for 360 ° or 180 ° + fan angle for a specific slice without using a contrast agent. On the other hand, in the dynamic CT imaging, in order to examine temporal changes of the contrast agent, a projection data set is collected for 360 ° or 180 ° + fan angle for a specific slice from an appropriate time after injecting the contrast agent. Is started, and for example, it is continuously repeated for a period of time until the time when the contrast medium almost completely flows out of a specific slice.

  A spatial distribution (CT image) of CT values of a specific slice is reconstructed by the reconstruction processing unit 36 based on the projection data collected in the simple CT imaging S1 (S2). The cerebral ischemia diagnosis support apparatus 40 processes this CT image to generate a contrast-enhanced image and a sulcus-enhanced image (S3). The contrast-enhanced image is an image in which the brain tissue region of the original image is color-coded in at least two types according to the CT value, and the low CT region corresponding to the ischemic region is emphasized by this color-coding. The cerebral groove-enhanced image is an image in which the cerebrospinal fluid region of the original image is color-coded in at least two types according to the CT value, and the sulcus region disappeared by ischemia is emphasized by this color-coding.

  The contrast-enhanced image is selectively displayed on the display unit 38 together with the sulcus-enhanced image. By the operation of the input device 39 by the operator, a frame line (first ROI) indicating the outline of the low CT value region (non-recoverable region) is designated on the contrast enhanced image or sulcus enhanced image (S4). FIG. 4A illustrates the first ROI. The first ROI is not limited to being manually input, and may be specified from the CT image by threshold processing or clustering processing. Data defining the first ROI is stored in the data storage unit 37 together with the contrast-enhanced image, the sulcus-enhanced image, and the original CT image.

  Based on the projection data collected in the contrast dynamic CT imaging S5, a plurality of CT images (a series of CT images) having different imaging times related to the same specific slice as in the simple CT imaging are reconstructed by the reconstruction processing unit 36 ( S6). In the CBP study processing unit 120, a series of CT images are processed, and CBP, CBV, which represents the local blood flow dynamics in the brain tissue, that is, the dynamics of blood flow passing through the capillaries in the local tissue, MTT and Err indexes are obtained for each pixel, and a map (brain blood flow image) of these indexes is generated (S7). The CBP map is selectively displayed on the display unit 38 together with the CBV, MTT, or Err map. By the operation of the input device 39 by the operator, a frame line (second ROI) indicating the outline of the low blood flow region is designated on the CBP map or the like (S8). FIG. 4B illustrates the second ROI. The second ROI is not limited to being manually input, and may be specified by threshold processing from any map. Data defining the second ROI is stored in the data storage unit 37 together with the CBP map and the like.

  Next, in S9, in the cerebral infarction expert system 50, as shown in FIG. 4C, the CT image reconstructed in S2 includes a low CT value region (first ROI), a low blood flow region (second ROI), and Are superimposed (S9) and displayed on the display unit 38 (S10). The cerebral infarction expert system 50 also includes the number of pixels in the first ROI from the number of pixels (area) in the first ROI, the number of pixels (area) in the second ROI, and the number of pixels (area) in the second ROI together with the image. The number of pixels in the difference area obtained by subtracting (area) may be displayed. Further, the ratio of the number of pixels (area) in the first ROI to the number of pixels (area) in the second ROI may be obtained and displayed.

  Thus, by using the two types of images of the CT value and the cerebral blood flow image and comparing the low CT value region and the low blood flow region, the diagnosis and treatment policy of acute cerebral infarction can be effectively and quickly performed. Can be determined.

  Next, cerebral ischemia diagnosis and cerebral blood flow diagnosis will be described in detail. FIG. 15 shows a typical work procedure for diagnosis and treatment policy determination of acute cerebral infarction related to the present embodiment as a reference example.

  As shown in FIG. 1, the cerebral ischemia diagnosis support apparatus 40 includes a cerebral ischemia analysis control unit 41, an image display direction correction unit 42, an image processing unit 43, an index calculation unit 44, and a guidance generation unit 45. The image display direction correction unit 42 corrects the display direction of the tomographic image initially generated from the volume data by the image processing unit 43. Actually, the image display direction correction unit 42 manually or automatically detects the center plane that separates the left and right brains from the initially generated tomographic image, and is perpendicular to the center plane that separates the left and right brains. A tomographic image of a smooth surface is generated from volume data by cross-section conversion processing (MPR).

  The image processing unit 43 processes the tomographic image (referred to as an original image) generated by the image display direction correction unit 42 to determine the presence or absence of bleeding and generate a contrast enhanced image and a sulcus enhanced image. The original image is displayed on the display unit 38 together with the contrast enhanced image and the cerebral sulcus enhanced image under the control of the system controller 29 or the CT-DE control unit 41. The contrast-enhanced image is an image in which the brain tissue region of the original image is color-coded into at least three types according to the CT value. The sulcus-enhanced image is an image in which the cerebrospinal fluid region of the original image is color-coded into at least two types according to the CT value. As a useful index for cerebral ischemia diagnosis representing symmetry between the left brain and the right brain, the index calculation unit 44, for example, the volume ratio between the left brain region and the right brain region, the left brain region and the right brain region of the original image, Area ratio, the correlation coefficient between the original image and the horizontally inverted image across the center line of the original image is calculated. The guidance generating unit 45 determines whether or not treatment (for example, thrombolytic therapy) is possible based on a combination of a plurality of diagnosis results determined by the user.

  FIG. 5 shows a flow of cerebral ischemia diagnosis support processing according to the present embodiment. First, scanning is started (S1). The scan is a helical scan or a volume scan that covers the head of the subject. Each time projection data corresponding to an angle required for reconstruction of one tomographic image is collected, tomographic images are reconstructed one after another in parallel with the scan (S2). A technique for reconstructing tomographic images one after another in parallel with this scanning is a known technique called real-time CT or CT fluoroscopy. When the collection of data in the planned range including the head of the subject is completed, the scan ends (S3).

  Along with the start of scanning, a function for cerebral ischemia analysis (CT-DE) is activated by the analysis control unit 41 (S4). The image processing unit 43 controlled by the analysis control unit 41 displays the tomographic image reconstructed by the real-time CT with, for example, five different colors in order to facilitate the discovery of the bleeding region (S5). ). The tomographic image reconstructed in real time is an image related to a plane perpendicular to the rotation axis (Z axis).

The region (mainly air and water) in which the CT value is in the range of −2051 or more and less than 0 is displayed in black.
A region (mainly white matter) having a CT value in the range of 0 to less than 32 is displayed in blue.
An area (mainly gray matter) having a CT value of 32 or more and less than 40 is displayed in blue.
A region (mainly a bleeding site) where the CT value is in the range of 40 or more and less than 200 is displayed in green.
A region (mainly bone) having a CT value in the range of 200 or more and less than 4048 is displayed in white.

  The area displayed in green is a place where the possibility of bleeding is high. The observer can acquire in advance rough information for cerebral ischemia diagnosis from tomographic images that are color-coded according to CT values and displayed in real time.

  After the scan is completed, cerebral ischemia diagnosis support processing is started under the control of the analysis control unit 41. First, the image display direction correction unit 42 corrects the image display direction (S6). As described above, the tomographic image reconstructed in real time is an image related to a plane perpendicular to the rotation axis (Z axis). The image display direction correction unit 42 corrects the display direction of a tomographic image reconstructed in real time (hereinafter referred to as a real time tomographic image). Actually, the image display direction correction unit 42 manually or automatically detects the center plane that separates the left and right brains from the real-time tomographic image, and the tomographic image of a plane perpendicular to the center plane that separates the left and right brains. Is generated from the volume data by cross-section conversion processing (MPR).

  The left and right brain center plane can be defined by at least two left and right brain center lines having different Z-axis positions. The left and right brain center lines are lines that separate the left brain (left hemisphere) and right brain (right hemisphere) of the brain within the slice plane (XY plane). The calculation method of the left and right brain center line will be described later. As shown in FIG. 6, n left and right brain center lines CL1, CL2,... CLn are calculated from tomographic images of n slice planes. As shown in FIG. 7, a search is made for a plane A that minimizes the sum of distances (shifts) with respect to the n left and right brain center lines CL1, CL2,. This plane A is defined as the left and right brain center plane. Here, in order to facilitate subsequent image generation processing, the volume data is moved and rotated (coordinate conversion) as shown in FIG. 8 so that the left and right brain central plane A coincides with the YZ plane. By cutting out the XY plane image from the coordinate-converted volume data, it is possible to generate a slice plane tomographic image in which the left and right brains are closest to each other. Cerebral ischemia causes partial brain swelling or atrophy. Therefore, in the diagnosis of cerebral ischemia, the left-right symmetry of the brain is important. When the slice plane is tilted to the left or right, the reliability of diagnosis based on the symmetry decreases. By generating a tomographic image using a plane perpendicular to the central plane of the left and right brain as a slice, the reliability of diagnosis based on the symmetry improves. The left and right hemispheres of the brain are almost symmetrical, and if the plane A, which is the center of symmetry, is perpendicular to the slice plane (XY plane), the brain depicted in the image is also left-right symmetric.・ By comparing the right hemisphere, it is possible to determine the discovery of low-absorption areas, brain swelling, and narrowing of the ventricles. Also, if the line that is the center of symmetry in the image coincides with the Y axis, the left and right hemispheres are easy to compare. Therefore, re-creating each image so that the plane A matches the YZ plane is very effective for interpretation.

  In the above description, the left and right brain center planes are automatically calculated, but may be set manually. In this case, as shown in FIG. 9, at least two slice tomographic images are generated, and at least three points on the center plane assumed by the operator are designated from each of the two slice tomographic images. The left and right brain center planes can be calculated from the specified three points. In addition, a three-dimensional brain image is generated from the volume data and displayed together with a translucent YZ plane. The left and right brain center plane assumed by the operator may coincide with the YZ plane by freely moving and rotating the three-dimensional image by manual operation by the operator.

  Next, a method for calculating the left and right brain center lines will be described. The calculation of the left and right brain center line is an algorithm for searching for a line having the highest symmetry between the right half and the left half of the image. Actually, the level of symmetry is obtained as a cross-correlation between a horizontally reversed image obtained by turning a tomographic image around an assumed left and right brain center line and an image tomographic image before inversion.

For example, in FIG. 10, assume that an image F has a width of Nx pixels in the X direction and Ny pixels in the Y direction. For CT images, Nx is typically 512, the same as Ny.
i = {0,1, ..., Nx-1}
j = {0,1, ..., Ny-1}
Here, xi and yj are defined as follows.
xi = [i- (Nx-1) / 2] dx
yj = [j- (Ny-1) / 2] dy
Dx and dy are widths between pixels in the x and y directions, respectively.

  Here, the image center line is initially set as the left and right brain center line, and the image G is created by horizontally inverting the image F around the left and right brain center line. Then, a point g (xi, yi) on the image G corresponding to the point f (xi, yi) on the image F is obtained. As shown in FIG. 9, g (xi, yi) is obtained from surrounding points by linear interpolation. Then, a correlation coefficient R (f, g) between f (xi, yi) and g (xi, yi) is obtained.

R (f, g) = Sfg / √ (Sf * Sg)
Mf = Σf (xi, yi) / (Nx * Ny)
Sf = Σ [{f (xi, yi) −Mf} ² ]
Mg = Σg (xi, yi) / (Nx * Ny)
Sg = Σ [{g (xi, yi) −Mg} ² ]
Sfg = Σ [{f (xi, yi) -Mf} * {g (xi, yi) -Mg}]
The expression of the left and right brain center line that maximizes the value of the correlation coefficient R (f, g) is obtained by an iterative method.

  In the image processing unit 43, the same processing as S5 is performed on the tomographic image (original tomographic image) of the XY plane perpendicular to the left and right brain central plane generated by the image display direction correcting unit 42, and the presence or absence of bleeding is determined. (S7). That is, from the original tomographic image, a region of a CT value range in which the CT value indicates bleeding (eg, a range of 40 or more and less than 200) is extracted, and it is determined whether or not the size of the region is larger than a predetermined size. . Specifically, the number of pixels in the area is compared with a threshold value. Instead of the number of pixels in this region, a CT value range (eg, a range of 40 or more and less than 200) indicating the bleeding with respect to the number of pixels having a CT value within the range of brain tissue (eg, 0 or more and less than 40). The CT value range indicating the bleeding for the ratio of the number of pixels having the CT value of (inside) or the number of pixels having the CT value in the range corresponding to the bone (for example, 200 or more) (for example, within the range of 40 or more and less than 200) The ratio of the number of pixels having a CT value of () may be compared with the corresponding threshold value.

  When the number of pixels in the region is equal to or greater than the threshold value, the region is determined as a region having a high possibility of bleeding, and when the number of pixels in the region is less than the threshold value, the region is It is determined that the possibility of bleeding is low and the possibility of noise is high. When it is determined that there is a high possibility of bleeding, the outline of the region is superimposed on the original tomographic image in a specific color (S8), and ischemia diagnosis support can be terminated.

  When there is no region in the CT value range where the CT value indicates bleeding (range of 40 or more and less than 200), or even if it exists, if the size of the region is smaller than a predetermined size, the possibility of bleeding is low And ischemia diagnosis support is continued.

  The image processing unit 43 processes the original image to generate a contrast-enhanced image and a sulcus-enhanced image, and the index calculation unit 44 calculates an index (index) effective for cerebral ischemia diagnosis from the original image (S9). , S10) and display as shown in FIG. 12 (S11).

  The contrast-enhanced image is an image in which the brain tissue region of the original image is color-coded in at least two types according to the CT value. The sulcus-enhanced image is an image in which the cerebrospinal fluid region of the original image is color-coded into at least two types according to the CT value. As a useful index for cerebral ischemia diagnosis representing symmetry between the left brain and the right brain, the index calculation unit 44, for example, the volume ratio between the left brain region and the right brain region, the left brain region and the right brain region of the original image, Area ratio, the correlation coefficient between the original image and the horizontally inverted image across the center line of the original image is calculated.

  The contrast-enhanced image is an image that is color-coded so that an X-ray low absorption region in a CT tomographic image can be easily observed. For example, as a color scale for facilitating ischemia determination, the CT tissue range of brain tissue (20 to 40) is used as a coloration target range, and color classification is performed in the following three types.

High (34-40) ... Red
Normal (27-33) Pink
Low (20 to 27) ... Blue In the case of a normal head image, the distribution of CT values is almost symmetrical, but in the ischemic region, a color corresponding to a lower CT value (blue) in the ischemic region. ) Is widely distributed. By observing this, the ischemic region can be determined. The CT value varies depending on the tomographic image creation conditions. Therefore, the CT value range of the brain tissue and the color classification may be automatically adjusted by the following method. As is well known, the CT value is highly dependent on the reconstruction function and the tube voltage. Therefore, a plurality of combinations of the reconstruction function and the tube voltage are associated with a plurality of combinations of the CT tissue range of the brain tissue and the color classification, and the tube voltage / reconstruction function is obtained from the incidental information of the image. When read, according to the combination, the CT value range of brain tissue and the classification of color coding are applied.

  A method of automatically determining the CT value range and color classification of the brain tissue from the CT value histogram may be adopted. In the image, the number of pixels per CT value of only the brain tissue pixels, with only pixels having values within a CT value range of, for example, 5 to 60, which are wide enough to allow variation in the CT value range of brain tissue as the processing range Create a histogram representing. Then, an X value in which the number of pixels equal to or greater than a certain CT value X corresponds to 40% of the total number of pixels in the brain tissue is obtained, and this is set as an upper limit threshold value for a region (blue region) where there is a high possibility of ischemia. Although it is not strictly the cortex that is detected by this threshold, it is sufficient for the purpose of finding a low absorption region (region with a low CT value). Here, the horizontal axis of the histogram is the CT value, and the vertical axis is the ratio [%] to the total number of pixels. When the left and right histograms are displayed in a graph, the left and right differences are easily compared.

The sulcus-enhanced image is an image in which the sulcus region is color-coded in order to observe the sulcus in a CT tomographic image. As a color scale that makes it easy to determine the sulcus, the CT value range (−200 to 20) of cerebrospinal fluid existing in the sulcus is used as a coloring target range, for example, the range is color-coded as follows: Change to color scale.
High (10-20) Orange
Low (−200 to 10)... Yellow In the case of a CT tomographic image of a normal head, the sulcus is depicted almost symmetrically. However, in the vicinity of the ischemic region, the sulcus becomes narrower due to swelling of the brain, and the sulcus is not drawn compared to the opposite side. By observing this, the ischemic region can be determined.

  Also, instead of emphasizing contrast by colorizing the sulci, the presence of sulci may be emphasized by drawing the outline of the brain. Under normal conditions, the contour line becomes a wavy and curved line due to the presence of the cerebral groove, but in the vicinity of the ischemic region, the cerebral groove is not drawn, so that the contour line is smooth and has no undulation. Here, for example, the contour line is divided into 2 parts on the left and right, 3 parts on the front, middle, and back, 6 parts in total are created, and by comparing these lengths, the presence or absence of cerebral sulcus is expressed numerically it can. Then, the short line is colored to emphasize the disappearing part of the sulcus. Thereby, the user can determine the ischemic area.

  As indexes effective for ischemia diagnosis, the degree of symmetry between the right brain region and the left brain region, the average CT value of each of the six regions (ROI) obtained by dividing the region near the brain surface around the body axis in the circumferential direction, The index calculation unit 44 calculates the pixel number distribution for each CT value of each ROI. In order to limit the region near the surface of the brain, the outline of the brain is first extracted. If this contour is a flat curve C, and a closed curve obtained by reducing C to about 70% is D, an annular region surrounded by C and D is formed. This area is divided into 6 areas (ROI) by dividing it into 2 parts on the left and right and 3 parts on the front, middle and back, for a total of 6 parts. Then, the average CT value of each of the six ROIs is calculated. The index calculation unit 44 further calculates a difference between the average CT value of the right front ROI and the average CT value of the left front ROI, and when the difference is larger than a predetermined threshold, the right side having a small average CT value is calculated. The front ROI or the left front ROI is set as a region having a high possibility of ischemia, and its outline is superimposed on all or any of the original image, the contrast-enhanced image, and the sulcus-enhanced image. Similarly, the average CT value is compared between the right and left sides in the middle region and the rear region of the toroidal region, and when the difference is larger than the threshold value, the region having a small average CT value is considered as a possibility of ischemia. As a high region, the outline is superimposed on all or any of the original image, contrast enhanced image, and sulcus enhanced image. Also, the average CT value of each of the six areas is displayed numerically.

  Here, the determination is made statistically using the number of pixels O in the region, the CT value variation SD, and the average CT value M. If there is a statistical difference, the outline that borders the region is colored to emphasize which region is ischemic. Thereby, the user can determine the ischemic area.

  The degree of symmetry between the right brain region and the left brain region is a parameter used for searching the left and right brain center lines. If the value of symmetry exceeds the threshold and deviates significantly from the normal value, it indicates that either the left or right brain is dilated or atrophied. In this case, the number of the symmetry is displayed in a color different from that in the normal state where the value of the symmetry is equal to or less than the threshold value, thereby emphasizing the presence or absence of expansion or contraction.

  In the above description, the symmetry of the left and right hemispheres is calculated in the two-dimensional region of the tomographic image. However, the comparison may be made in the volume (three-dimensional region) of the left and right brain tissues. Similar to the two-dimensional case, the left and right three-dimensional regions are interchanged with the left and right brain center planes sandwiched, and the correlation coefficient is calculated with the volume before the replacement. Further, as the degree of symmetry, a difference (or ratio) of the volume (or area) between the left brain region and the right brain region may be simply adopted. When the volume or area difference or ratio exceeds a predetermined threshold, the volume or area is displayed together with the difference or ratio in a color different from that when the difference or ratio is less than or equal to the threshold. Emphasize that there is atrophy.

The initial slice position of the image displayed on the display screen in FIG. 12 is determined as follows. The control unit 41 detects a slice in which a basal nucleus having a high clinical value is depicted by one of the following or any combination thereof.
Slice 2cm above the slice where the upper side of the ear disappears
Slices that have four ventricles, each with a specific size
A slice in which the Sylvian fissure looks like a certain size Next, diagnostic guideline display by the guideline generator 45 will be described. For example, a button labeled “Guide for diagnosis / treatment” is displayed on the screen of FIG. By clicking this button, guidelines for cerebral ischemia diagnosis are displayed as shown in FIGS. In the guideline, a plurality of user check items for cerebral ischemia diagnosis are prepared together with remarks. Examples of these items are:
Does the lens nucleus have a low absorption range? (Remarks: Ischemia in the future, but can still be treated with thrombolysis)
Is there a low absorption area in the cortical area? (Remarks: There is an ischemic region. Thrombolysis treatment is impossible.)
Is there a loss of sulci? (Remarks: There is an ischemic region. Thrombolysis treatment is impossible.)
Is your brain distorted? (Remarks: Brain swelling and atrophy. Thrombolytic treatment is impossible.)
How long has it been onset? (Remarks: After 6H or more, there is a high possibility of bleeding through reopening.)
The guideline generation unit 45 associates the combination of answers to these check items with the presence or absence of ischemic changes (ischemic diagnosis result) and the applicability of treatment (for example, thrombolytic therapy) (notes on treatment). I remember. The presence or absence of an ischemic change corresponding to the combination of answers to check items and whether or not treatment (thrombolytic therapy) can be applied are displayed.

  Next, diagnosis by cerebral blood flow (CBP study) will be described. As shown in FIG. 1, the CBP study processing unit 120 includes an ROI setting support unit 121, a time concentration curve creation unit 122, a cerebral artery time concentration curve correction unit 123, an MTF processing unit 124, an index calculation unit 125, and a map creation unit. 126 and a map composition unit 127. Briefly describing the CBP study, the CBP study is a method for calculating an index (index) related to blood flow dynamics of capillaries in brain tissue. In the CBP study, indices such as CBP, CBV, MTT, and Err that quantitatively represent the local blood flow dynamics in the tissue, that is, the blood flow dynamics through the capillaries in the local tissue are obtained. Output the map.

  CBP represents the unit volume in the capillary of the brain tissue and the blood flow volume per unit time [ml / 100 ml / min], CBP represents the blood volume per unit volume in the brain tissue [ml / 100 ml], and MTT represents The blood average transit time [seconds] of the capillary blood vessel is represented, and Err represents the sum of the residuals or the square root of the squared sum of the residuals when approximating the transfer function.

  Indexes CBP, CBV, and MTT that quantitatively represent the blood flow dynamics of the capillaries in these brain tissues, together with information on the elapsed time since the onset of cerebral ischemic stroke, are used for pathological differentiation of ischemic cerebrovascular disorders. It is expected as useful information for the evaluation of the presence or absence of enlargement of capillaries, blood flow velocity, and the like. For example, generally in an ischemic cerebrovascular disorder, the blood pressure of the provided artery is reduced, and the blood flow velocity in the blood vessel is reduced. As a result, even if CBV is constant, MTT is prolonged and CBP is lowered. In the cerebral infarction hyperacute phase, in order to compensate for the decrease in blood flow rate due to the decrease in blood pressure, the capillaries are expanded and the blood flow rate is increased to suppress the decrease in blood flow CBP (auto Regulation). Therefore, even if CBP decreases due to the extension of MTT, if CBV increases, it is information suggesting the possibility of reopening of capillaries.

  In the CBP study, a contrast agent having no cerebral vascular permeability, such as an iodine contrast agent, is used as a tracer. The iodine contrast agent is injected from the cubital vein by an injector, for example. The iodine contrast medium intravenously injected by the injector flows into the cerebral artery via the heart and lungs. Then, the contrast medium flows out from the cerebral artery to the cerebral vein through the capillaries in the brain tissue. At this time, the iodine contrast medium passes through the capillary blood vessels in normal brain tissue without leaking out of the blood vessels. FIG. 16 schematically shows this state.

  The state of passage of the contrast agent is photographed by dynamic CT, and from the continuous image, the time density curve Ca (t) of the pixel on the cerebral artery and the time density curve Ci (t) of the pixel on the brain tissue (capillary blood vessel) are obtained. The time density curve Csss (t) of the pixel on the cerebral vein is measured.

  Here, in the CBP study, as shown in FIG. 17, an ideal relationship that is established between the concentration time curve Ca (t) of the cerebral artery and the concentration time curve Ci (t) of the brain tissue is used as the analysis model. When contrast medium is injected from a blood vessel just before entering the brain tissue, the time density curve in the unit volume (1 pixel) of the brain tissue is vertical, maintains a constant value for a while, and then rises steeply. It goes down. This is approximated by a rectangular function (box-MTF method: box-Modulation Transfer Function method).

  That is, the transfer function between the input function and the output function is approximated by a rectangular function using the time concentration curve Ca (t) of the cerebral artery as an input function and the time concentration curve Ci (t) of the brain tissue as an output function. The transfer function represents the process by which the tracer passes through the capillaries.

  The time concentration curve creation unit 122 generates time concentration curves related to cerebral arteries, cerebral veins, and brain tissues (capillaries) from dynamic CT image data (a plurality of temporally continuous image data) stored in the storage device 10M. create. The cerebral artery time concentration curve Ca (t) is individually created for, for example, six cerebral arteries ROI that have been set. The cerebral venous time concentration curve Csss (t) is created for the cerebral venous ROI set in the upper sagittal sinus. Further, the time density curve Ci (t) of the brain tissue is created for each pixel for all the pixels on the brain tissue.

  The cerebral artery time concentration curve correction unit 123 uses the cerebral artery time concentration curve Ca (t) based on the upper sagittal sinus time concentration curve Csss (t) in order to remove the influence of noise and the partial volume effect. To correct. This correction method will be described later. The MTF processing unit 124 converts the transfer function MTF into the brain tissue region by the box-MTF method based on the corrected time concentration curve Ca (t) of the cerebral artery and the time concentration curve Ci (t) of the brain tissue. The calculation is performed for each pixel with respect to all the pixels.

  The index calculation unit 125 calculates an index (CBP, CBV, MTT, Err) representing the blood flow dynamics of the brain tissue from the calculated transfer function MTF for every pixel in the brain tissue region. As shown in FIG. 18, the map creation unit 126 generates a map of each calculated index for each cerebral artery (ACA, MCA, PCA).

  As shown in FIG. 15, by combining a diagnosis using a CT value image and a diagnosis using a cerebral blood flow image, it is possible to determine a diagnosis and treatment policy with higher accuracy than a diagnosis using only one of them. . In particular, the low CT value region from the CT value image can be estimated as an unrecoverable region. The non-recoverable area is included in the low blood flow area, and the area outside the non-recoverable area within the low blood flow area can be regarded as a highly recoverable area. The size of this area is used as an important judgment material for the therapeutic effect.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. In addition, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

1 is a diagram showing a schematic configuration of an X-ray computed tomography apparatus equipped with an acute cerebral infarction diagnosis treatment support apparatus according to an embodiment of the present invention. The figure which illustrates two types of images by this embodiment. The flowchart of the acute cerebral infarction diagnosis treatment support operation | movement by this embodiment. FIG. 4 is a supplementary diagram of S4, S8, and S9 of FIG. The flowchart of the ischemia diagnosis support operation | movement using CT image in the acute cerebral infarction diagnosis treatment support operation | movement by this embodiment. The figure which illustrates a brain centerline in order to supplement the image display direction correction process of FIG. 6 is a diagram illustrating a center plane A that separates the left and right brains defined from the center line of FIG. 6 in order to supplement the image display direction correction processing of FIG. The figure which shows the coordinate correction of the volume data based on a center plane in order to supplement the image display direction correction process of FIG. The figure which shows the manual designation | designated method of the center plane of FIG. Explanatory drawing of the automatic detection method of the centerline of FIG. The figure which illustrates the interpolation process for the production | generation of the horizontally reversed image used in the centerline automatic detection method of FIG. The figure which shows the example of a display screen of S11 of FIG. The figure which shows the example of a display screen of S12 of FIG. FIG. 14 is a detailed view of the guideline of FIG. 13. The figure which illustrates the flow of the operation | work for the acute cerebral infarction diagnosis treatment using the acute cerebral infarction diagnosis treatment support apparatus by this embodiment. FIG. 2 is a diagram illustrating the principle of a CBP study performed by the CBP study processing unit of FIG. 1. Explanatory drawing of the MTF process by the CBP study process part of FIG. The figure which illustrates the cerebral blood flow image (CBP map etc.) by the CBP study process part of FIG.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Base, 2 ... Bed, 3 ... Computer unit, 10 ... X-ray tube, 12 ... Rotating frame, 21 ... High voltage generator, 23 ... X-ray detector, 25 ... Base drive device, 26 ... Data collection device, DESCRIPTION OF SYMBOLS 29 ... System controller, 30 ... Scan controller, 34 ... Pre-processing part, 36 ... Reconstruction processing part, 37 ... Data storage part, 38 ... Display part, 39 ... Input device, 40 ... CT-DE apparatus (cerebral ischemia analysis) Device), 41 ... CT-DE control unit, 42 ... image display direction correction unit, 43 ... image processing unit, 44 ... index calculation unit, 45 ... guidance generation unit, 120 ... CBP study processing unit.

Claims (5)

A storage unit that stores a CT image relating to a cross section of the subject's head and a cerebral blood flow image representing a spatial distribution of a cerebral blood flow dynamic index relating to substantially the same cross section of the subject;
An image generating unit that generates a contrast-enhanced image that emphasizes a region of a specific CT value range from the CT image, or a sulcus-enhanced image that emphasizes a sulcus region from the CT image;
An image processor that superimposes a first ROI related to ischemia identified from the contrast-enhanced image or the sulcus-enhanced image and a second ROI related to low blood flow identified from the cerebral blood flow image on the CT image;
An acute cerebral infarction diagnosis treatment support device comprising: a display unit that displays the CT image on which the first ROI and the second ROI are superimposed. Image generation for calculating and displaying blood flow volume per unit volume and unit time in capillaries of brain tissue, blood volume per unit volume in brain tissue or blood mean passage time of capillaries as the brain blood flow dynamic index The acute cerebral infarction diagnosis and treatment support apparatus according to claim 1, further comprising a unit. The acute cerebral infarction diagnosis treatment support apparatus according to claim 1, further comprising a calculation unit that calculates an area ratio of a difference area between the second ROI and the first ROI with respect to the second ROI. 2. The acute cerebral infarction diagnosis and treatment support apparatus according to claim 1, wherein a difference area between the second ROI and the first ROI with respect to the second ROI is colored and highlighted. The acute cerebral infarction diagnosis treatment support apparatus according to claim 1, further comprising an operation unit for an operator to designate and operate the first and second ROIs on the contrast-enhanced image or the cerebral sulcus-enhanced image .

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