JP2014046034A - Image processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processor capable of properly displaying a nerve fiber.SOLUTION: An image processor relating to an embodiment includes an analysis part, an association part, a determination part, and a display control part. The analysis part generates DWI (Diffusion Weighted Image) volume data holding running information of a nerve fiber in each voxel. The association part associates a spatial location of non-DWI volume data holding state information of an object in each voxel with spatial location of the DWI volume data. The determination part determines a display mode of the nerve fiber that passes through a voxel in a DWI volume data whose spatial location corresponds to the voxel in accordance with state information held in the non-DWI volume data. The display control part displays a nerve fiber derived from the DWI volume data according to the display mode determined in each nerve fiber.

Description

  Embodiments described herein relate generally to an image processing apparatus.

  Conventionally, there is a technique called tractography analysis. The tractography analysis is based on diffusion-weighted image data (hereinafter referred to as DWI (Diffusion Weighted Image) volume data) collected by an MRI (Magnetic Resonance Imaging) apparatus, and isotropic or different diffusion of water molecules present in brain tissue. This is a technology that analyzes the directionality and estimates the running of nerve fibers.

  Tractography analysis is used for evaluation before brain tumor removal surgery. For example, in malignant brain tumor removal surgery, the survival rate is improved by bringing the removal rate close to 100%, while nerve fibers that are responsible for important functions such as language and exercise may be damaged by the removal operation. For this reason, there is a need to clarify the positional relationship between nerve fibers responsible for important functions and brain tumors before surgery.

Hangyi Jiang, et al. “DtiStudio: Resource program for diffusion tensor computation and fiber bundle tracking”, COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 8I (2006) 106-116 Susumu Mori, et al. “Fiber tracking: principles and strategies-a technical review”, NMR IN BIOMEDICINE 2002; 15; 468-480

  The problem to be solved by the present invention is to provide an image processing apparatus capable of appropriately displaying nerve fibers.

  The image processing apparatus according to the embodiment includes an analysis unit, an association unit, a determination unit, and a display control unit. The analysis unit generates DWI volume data in which nerve fiber running information is held in each voxel by tractography analysis. The associating unit associates the spatial position of the DWI volume data with the non-DWI volume data in which the same object as the DWI volume data is imaged and the state information of the object is held in each voxel. The determination unit determines a display mode of nerve fibers passing through the voxels in the DWI volume data corresponding to the spatial positions of the voxels according to the state information held in the voxels in the non-DWI volume data. . The display control unit displays the nerve fiber derived from the DWI volume data on the display unit according to the display mode determined for each nerve fiber by the determination unit.

FIG. 1 is a diagram showing the flow of processing in the first embodiment. FIG. 2 is a diagram showing a fiber display in the first embodiment. FIG. 3 is a diagram illustrating a configuration of the image processing apparatus 10 according to the first embodiment. FIG. 4 is a diagram illustrating an fMRI analysis result in the first embodiment. FIG. 5 is a diagram showing a display mode definition table in the first embodiment. FIG. 6 is a diagram for explaining the determination of the display mode in the first embodiment. FIG. 7 is a view for explaining the fiber display in the first embodiment. FIG. 8 is a diagram for explaining another example of the fiber display in the first embodiment.

  The image processing apparatus according to the embodiment will be described below. In the image processing apparatus according to the embodiment, when displaying nerve fibers (hereinafter referred to as fibers) derived from DWI volume data on the display unit, the display mode of the fibers according to information held in other non-DWI volume data Is determined for each fiber, and each fiber is displayed according to the determined display mode. The display mode is, for example, display color, color shading, line type, and the like. The non-DWI volume data is, for example, volume data collected by the MRI apparatus, an analysis result thereof, and the like. Note that the image processing apparatus is not limited to the following embodiment.

(First embodiment)
The image processing apparatus 10 according to the first embodiment determines the display mode of each fiber according to functional field information (hereinafter, task information) held in the analysis result of the f (functional) MRI analysis.

  Here, the fMRI analysis is a technique for specifying the position on the brain surface of a functional field such as a language field or a motor field for each subject. Specifically, in fMRI analysis, the brain is imaged by the MRI apparatus while giving a task such as speech or tapping to the subject, and blood between the MRI volume data at the time of task execution and the MRI volume data at the time of non-execution is obtained. The difference in the flow signal value is tested, and voxels having a significant difference of a certain level or more are regarded as functional areas, and the brain activation area is specified for each task. Further, by linking fMRI analysis and tractography analysis, an activation region obtained as an analysis result of fMRI analysis can be designated as a fiber tracking start point. In this case, the image processing apparatus can display the fiber that passes through the activated region.

  However, the fiber corresponding to the task cannot be identified from the displayed fiber. The identification of the activated region in the fMRI analysis is an analysis result by statistical processing. That is, the specified activated region includes a region having a high possibility of being activated and a region having a low possibility of being activated, but it is not possible to identify the statistical probability from the displayed fiber.

  This point will be described in more detail. Functional areas are on the surface of the brain. For this reason, even in the analysis result of the fMRI analysis, the activated region is often specified near the surface of the brain. On the other hand, what the surgeon wants to know before surgery to remove a brain tumor is not only the functional area on the surface of the brain, but also what kind of fiber goes into the brain from the functional area to the body. This is information indicating whether the vehicle is traveling along a route.

  As described above, if the activation region obtained as the analysis result of the fMRI analysis can be designated as the fiber tracking start point, the fiber passing through the functional field can be displayed. However, the analysis result of the fMRI analysis does not make sense unless it is set with task information indicating what kind of task is executed by the subject. For example, it is only found that an area that is presumed to have some function has been found. It is unclear whether it is related to speech, finger tapping, visual, or some combination. Even if the activation area obtained as an analysis result of the fMRI analysis is specified as the fiber tracking start point, if the task information is not set, the user must analogize the function of the fiber by experience. I must.

  In addition, it is difficult to accurately reflect the meaning of the analysis result of the fMRI analysis in the analysis result of the tractography analysis. This is because it is only possible to cause a misunderstanding that the area displayed as the activation area is absolutely a functional field, and the other areas are not absolutely functional fields. Since the fMRI analysis is an analysis based on statistical processing, the final result (activation area) only indicates an area that exceeds a certain significant difference. For example, if the significant difference is 0.05, a region “which can be said to have been activated with a probability of 95%” is indicated. When the significant difference is changed to 0.06, the activated region expands. Alternatively, a new activation area appears. For this reason, there may occur a situation in which displayed fibers are different only by slightly changing the analysis conditions of the fMRI analysis. In surgery to remove a tumor, once a patient's nerve fibers have been damaged, there is a risk of leaving serious permanent complications (lifelong speech or part of the body). For this reason, there is a need to examine the running direction of a patient's nerve fibers before surgery. However, when the above situation occurs, a slight difference in the analysis results overlooks the fiber that should have existed, or leaves a brain tumor that could have been removed in an attempt to protect the fiber that did not really exist. It can happen.

  For this reason, the image processing apparatus 10 according to the first embodiment determines the color of each fiber according to the task information held in the analysis result of the fMRI analysis, and the statistical reliability of the analysis result. The shade of the color is determined according to.

  FIG. 1 is a diagram showing the flow of processing in the first embodiment. Here, only the flow of processing in the first embodiment will be briefly described, and details of each processing will be described later. In the first embodiment, for example, in radiology, collection of DWI volume data and fMRI volume data by an MRI apparatus and fMRI analysis by an MRI apparatus are performed (within a dotted line frame in FIG. 1), and surgery is performed. Suppose a situation in which processing by the image processing apparatus 10 is performed. However, the embodiment is not limited to this, and it is possible to arbitrarily change which device of which family the collection of various volume data, various analyzes, and other processes are executed.

  First, a subject is imaged by an MRI apparatus (not shown), and DWI volume data and volume data for fMRI analysis (hereinafter, fMRI volume data) are collected. In the first embodiment, the fMRI volume data includes, for example, fMRI volume data (speech) collected while giving a speech task to the subject, and fMRI volume data (tapping) collected while giving the tapping task to the subject. ).

  In the first embodiment, since the imaging sequence for collecting DWI volume data is different from the imaging sequence for collecting fMRI volume data, the DWI volume data and the fMRI volume data are separately captured and collected separately. However, first, a plan image (also referred to as a positioning image, Locator, etc.) is collected, and then an imaging range of DWI volume data is designated and collected on the plan image, and fMRI volume data is collected on the same plan image. Since the imaging range is designated and collected, the spatial positional relationship between these volume data is known. Note that the imaging order is arbitrary.

  Next, as shown in FIG. 1, fMRI analysis is performed on the fMRI volume data, and an analysis result for each task is generated. In the first embodiment, the analysis result for each task (hereinafter, “fMRI analysis result”) corresponds to non-DWI volume data.

  Subsequently, as illustrated in FIG. 1, the image processing apparatus 10 generates DWI volume data in which fiber travel information is held in each voxel by tractography analysis on the DWI volume data. Then, the image processing apparatus 10 associates the spatial positions of the DWI volume data after the tractography analysis, the fMRI analysis result (speech), and the fMRI analysis result (tapping).

  Next, the image processing apparatus 10 displays a fiber that passes through the voxel in the DWI volume data in which the spatial position corresponds to the voxel according to the task information and the statistical value held in the voxel in the fMRI analysis result. To decide. Specifically, the image processing apparatus 10 determines the color of each fiber according to the task information held in the voxel in the fMRI analysis result, and determines the color of the color according to the reliability of the analysis result, that is, the statistical value. Determine the tint.

  For example, in FIG. 1, activation regions a1, a2, a3, and a4 are voxels corresponding to task information “speech”. The activation areas b1, b2, and b3 are voxels corresponding to the task information “tapping”. Therefore, the image processing apparatus 10 assigns a “blue” color (a dark black line in FIG. 1) to the fibers passing through the activation regions a1, a2, a3, and a4, and passes through the activation regions b1, b2, and b3. Assign a “red” color (light gray line in FIG. 1) to the fibers that do. Further, the image processing apparatus 10 assigns “dark blue” or “dark red” to the fiber passing through the activation region with high reliability according to the statistical value held in each voxel, and the reliability is low. Assign "light blue" or "light red" to the fiber that passes through the activation region. In FIG. 1, the color shading is indicated by the difference between the solid line and the dotted line. Moreover, although the color assignment and the color shade assignment are arbitrary, for example, it is desirable to select an intuitively easy-to-understand assignment such as making the color with higher reliability darker.

  Thereafter, the image processing apparatus 10 displays the fiber derived from the DWI volume data on the display unit according to the display mode determined for each fiber. FIG. 2 is a diagram showing a fiber display in the first embodiment. As shown in FIG. 2, the image processing apparatus 10 displays the fiber derived from the DWI volume data on the display unit in a display mode that is different for each fiber, and also displays correspondence information indicating the correspondence between each fiber and the task information. And displayed on the display unit.

  In FIG. 2, the difference in color is indicated by a solid line and a dotted line. For example, the image processing apparatus 10 assigns “blue” color (dotted line in FIG. 2) to the fiber passing through the voxel corresponding to the task information “speech”, and assigns the fiber passing through the voxel corresponding to the task information “tapping”. Assigns a “red” color (solid line in FIG. 2). Further, the image processing apparatus 10 assigns “dark blue” or “dark red” to the fiber that passes through the activation region with high reliability (the dark black solid line or dotted line in FIG. 2), and the activity with low reliability. The fibers that pass through the conversion region are assigned “light blue” or “light red” (light gray solid line or dotted line in FIG. 2). Further, as shown in FIG. 2, the image processing apparatus 10 corresponds to “blue” color (dotted line in FIG. 2) and “speech”, and “red” color (solid line in FIG. 2) and “tapping”. ”And corresponding information are also displayed. For example, the characters “speech” may be displayed in blue and the characters “tapping” may be displayed in red. Further, a color bar may be displayed together to indicate the correspondence between the reliability level and the color density.

  FIG. 3 is a diagram illustrating a configuration of the image processing apparatus 10 according to the first embodiment. The DWI volume data storage unit 11, the non-DWI volume data storage unit 12, and the display mode definition storage unit 13 described below include a semiconductor memory device such as a RAM (Random Access Memory) and a flash memory, and a hard disk. It is realized with an optical disc or the like. The analysis unit 14, the association unit 15, the determination unit 16, and the display control unit 17 described below include an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), and a CPU (Central Processing). Unit) and MPU (Micro Processing Unit).

  The DWI volume data storage unit 11 stores DWI volume data. For example, the image processing apparatus 10 acquires DWI volume data collected by an MRI apparatus (not shown) via a network (not shown) and stores it in the DWI volume data storage unit 11. Note that the image processing apparatus 10 may acquire the DWI volume data by, for example, an input via a storage medium by an operator.

  The non-DWI volume data storage unit 12 stores non-DWI volume data. The non-DWI volume data is volume data in which the same object as the DWI volume data is imaged and the state information of the object is held in each voxel, and is the fMRI analysis result for each task in the first embodiment. . That is, the fMRI analysis result is volume data in which state information indicating the possibility of being an activated region is held in each voxel. For example, the image processing apparatus 10 acquires an fMRI analysis result for each task generated by an MRI apparatus (not shown) via a network (not shown) and stores it in the non-DWI volume data storage unit 12. Note that the image processing apparatus 10 may acquire the fMRI analysis result for each task by, for example, an input via a storage medium by an operator.

  FIG. 4 is a diagram illustrating an fMRI analysis result in the first embodiment. For example, as shown in FIG. 4, the non-DWI volume data storage unit 12 stores a task type and an analysis result in association with each other, and also associates an area ID, a voxel ID, and a p-value as an analysis result. And remember.

  “Task” shown in FIG. 4 is task information indicating the type of task executed by the subject when fMRI volume data is collected by the MRI apparatus. In general, medical image data is handled in a structure in which incidental information is attached to the image data itself in accordance with DICOM (Digital Imaging and Communications in Medicine) standards. The incidental information can include, for example, examination date and time, modality ID, patient ID, and various conditions at the time of imaging. For example, it is assumed that a rule for storing a task type is provided in advance at a specific address in the header of medical image data. In this case, when the fMRI volume data is collected, the MRI apparatus stores the task type input by the operator in the specific address of the header as supplementary information of the collected fMRI volume data. After that, the MRI apparatus that executes the fMRI analysis reads the task type from the specific address in the header, associates the read task type with the analysis result including the area ID, voxel ID, and p value, and displays the fMRI analysis result. It only has to be generated.

  “Area ID” shown in FIG. 4 is a number for identifying an activated area. For example, when there are 20 activated regions, the MRI apparatus that executes fMRI analysis assigns “0” to “19” as region IDs. In addition, the MRI apparatus that executes the fMRI analysis performs a labeling process so that different area IDs are assigned to activated areas that are not connected to each other. The “voxel ID” shown in FIG. 4 is a number for identifying a voxel belonging to each activated area. For example, when 20 slices of 128 × 128 images are captured as fMRI volume data by the MRI apparatus, the number of voxels is 128 × 128 × 20. In this case, for example, the MRI apparatus assigns numbers up to “128 × 128 × 20-1” in order, with the upper left of the image slice being “0”.

  The “p value” shown in FIG. 4 is information indicating the reliability of the analysis result. For example, the p-value of a voxel determined as an activated region with a significant difference “0.05” is “0.05 (activated region with a probability of 95%)”. A lower value of voxel is more likely to be an activated region. In the first embodiment, the example in which the p value is used as information indicating the high reliability of the analysis result has been described. However, the embodiment is not limited thereto. As information indicating the high reliability of the analysis result, a t value obtained directly by the test process may be used, or both the p value and the t value may be used. When displaying the fiber, the p value and / or the t value may be used so that the display mode can be changed according to the possibility of being an activated region.

  The display mode definition storage unit 13 stores the definition of the display mode when displaying the fiber. FIG. 5 is a diagram showing a display mode definition table in the first embodiment. As will be described later, in the first embodiment, the image processing apparatus 10 determines the color of the fiber and the density of the color according to the task information and the statistical value (p value). For this reason, for example, the display mode definition storage unit 13 stores, for each task, a display mode definition table in which statistical values (p values) and RGB are defined in association with each other. Note that the display mode definition table for each task stored in the display mode definition storage unit 13 is prepared in advance by an operator, for example, and stored in the display mode definition storage unit 13 in advance.

  The analysis unit 14 generates DWI volume data in which fiber travel information is held in each voxel by tractography analysis. Specifically, the analysis unit 14 reads DWI volume data from the DWI volume data storage unit 11 and performs tractography analysis on the read DWI volume data. The analysis unit 14 calculates a value indicating whether or not a fiber is present for each voxel of the DWI volume data by tractography analysis, and indicates a direction in which the fiber is directed. Each voxel is held as information. The analysis unit 14 can perform tractography analysis by applying a known technique.

  The associating unit 15 associates the spatial position between the DWI volume data after the tractography analysis and the fMRI analysis result for each task. Specifically, the associating unit 15 associates the spatial position between the DWI volume data subjected to tractography analysis by the analyzing unit 14 and the fMRI analysis result for each task read from the non-DWI volume data storage unit 12.

  Here, the imaging range of the DWI volume data and the fMRI volume data are not always the same. Also, the pixel size, slice thickness, gap between slices, and the like may be different. For this reason, the voxel ID of the DWI volume data and the voxel ID of the fMRI analysis result may indicate different spatial positions. For this reason, the correlation part 15 matches both voxel IDs so that the same spatial position may be shown. Note that the association unit 15 can perform association by applying a known technique. Hereinafter, two methods will be described.

  Method 1 is a method using header information that is supplementary information. In general, in the header of medical image data, information indicating where in the apparatus coordinate system the upper left position is stored for each captured image slice. The association unit 15 can associate a spatial position between the voxel ID of the DWI volume data and the voxel ID of the fMRI analysis result by using this information. Method 2 is a method using image processing. The process of converting one image as a reference and converting the other image to align with the reference image is widely performed for medical images, and various algorithms have been proposed according to applications and purposes. The association unit 15 converts the voxel ID of the fMRI analysis result into the voxel ID of the DWI volume data as a result of this image processing (the task, the region ID, and the p value do not need to be converted). In addition, when the pixel sizes of the DWI volume data and the fMRI analysis result do not match, the associating unit 15 assigns, for example, a voxel ID for 1 voxel of the fMRI analysis result to each voxel ID of 4 voxels of the DWI volume data. You may interpolate by assigning.

  The determination unit 16 determines the display mode of the fiber passing through the voxel in the DWI volume data corresponding to the spatial position of the voxel according to the state information held in the voxel in the non-DWI volume data. In the first embodiment, the non-DWI volume data is an fMRI analysis result for each task, and the state information is task information and a statistical value indicating the possibility of being an activated area.

  First, the determination unit 16 applies a known technique, designates an activation region obtained as an fMRI analysis result as a fiber tracking start point, and performs fiber tracking. Fiber tracking is a technique that starts tracking from a starting point, and ends tracking when, for example, a FA (Fractional Anisotropy) value falls below a predetermined threshold or when the fiber bends more than a predetermined angle. For example, the determination unit 16 uses each voxel belonging to each region ID of the fMRI analysis result as an ROI (Region On Interest), that is, a fiber tracking start point. As a result, the determination unit 16 can track a fiber that runs into or starts running in a location that is regarded as an activated region based on the fMRI analysis result.

  The determination unit 16 handles each region ID of the fMRI analysis result independently. That is, fMRI analysis results often include obvious noise and regions that can be clearly excluded. As an example of the former, for example, there is a case where a voxel that is clearly outside the brain is determined as an activated region. As an example of the latter, for example, there is a function activation region that appears when the subject has done something other than the task being executed during imaging. For example, even when performing speech tasks, it is difficult to stop working other than language functions. For this reason, the visual function and the auditory function often work. In such a case, the determination unit 16 treats each area ID independently so that only the fiber related to the target functional field can be displayed and the others can be hidden.

  Now, the fiber travel you want to check before brain tumor surgery is not limited to one function. For example, you may want to check both speech fibers and motor fibers. In this case, fMRI volume data imaging and fMRI analysis for specifying the location of each functional field are performed, and two analysis results are obtained. Either analysis result can be used as the starting point of fiber tracking by the above-described method, but the determination unit 16 changes the display mode of the fiber so that a user viewing the fiber tracking result can distinguish between the two. .

  Specifically, the determination unit 16 refers to the display mode definition storage unit 13, refers to a display mode definition table that varies depending on the task, and determines the display mode of each fiber. For example, the determination unit 16 uses each voxel belonging to each region ID of the fMRI analysis result as a fiber tracking start point, but determines the color of each fiber according to the task information associated with each voxel. For example, when “speech” is stored as an fMRI analysis result in association with a voxel that is the starting point of a certain fiber, the determination unit 16 refers to the display mode definition table for the “speech” task. . On the other hand, when “tapping” is stored in association with the voxel that is the starting point of a certain fiber as the fMRI analysis result, the determination unit 16 refers to the display mode definition table for the “tapping” task. .

  Furthermore, the determination unit 16 according to the first embodiment determines the color shading according to the reliability of the analysis result, that is, the statistical value (p value). Specifically, when one fiber passes through a plurality of voxels in the DWI volume data, the determination unit 16 identifies and identifies the voxel corresponding to the plurality of voxels from the fMRI analysis result. A representative statistical value (p value) is derived from a plurality of statistical values (p value) held by a plurality of voxels. Then, the determination unit 16 determines the color density according to the representative statistical value (p value).

  FIG. 6 is a diagram for explaining the determination of the display mode in the first embodiment. As shown in FIG. 6, the determination unit 16 firstly, for each fiber, out of a plurality of voxels passing from the start point to the end point of the fiber tracking, the voxel belonging to the activated region in the fMRI analysis result (in FIG. 6) (Shaded voxels). Next, the determination unit 16 acquires a statistical value (p value) from the fMRI analysis result for each identified voxel, and derives a minimum value among the acquired statistical values (p value). For example, the determination unit 16 derives “0.01” as the p value that is the minimum value for the fiber 1, and derives “0.15” as the p value that is the minimum value for the fiber 2.

  A fiber that passes through a voxel having a small p-value is considered to have a high probability of being present. Therefore, for example, it is desirable to display it in a dark color (for example, dark red) so as to stand out. On the other hand, a fiber that passes only voxels having a large p-value is considered to have a relatively low probability, so that it is desirable to display it in a light color (for example, light pink), for example. The intermediate p value may be changed stepwise from a dark color to a light color according to the value.

  Since such color shading is defined in the display mode definition table, when the determination unit 16 derives the p value that is the minimum value, the determination unit 16 refers to the display mode definition table using the derived p value, The RGB stored together may be determined as the display mode of this fiber. For example, for the fiber 1, the determination unit 16 refers to the display mode definition table using the p value “0.01” and determines RGB = (255, 0, 0) as the display mode. For the fiber 2, the determination unit 16 refers to the display mode definition table using the p value “0.15” and determines RGB = (255, 85, 85) as the display mode.

  The display control unit 17 displays each fiber on the display unit 18 such as a liquid crystal display according to the display mode determined for each fiber by the determination unit 16. FIG. 7 is a view for explaining the fiber display in the first embodiment. For example, as shown in FIG. 7, the display control unit 17 displays the fiber 1 with RGB = (255, 0, 0), and the fiber 2 has a color shade different from that of the fiber 1. Display with RGB = (255, 85, 85). As shown in FIG. 7, the display control unit 17 also displays a color bar indicating the correspondence between the p value, the color of the fiber, and the shade of the color. As described above, the fiber display by the display control unit 17 is displayed as shown in FIG. 2, for example. Both the correspondence information between the task and the color and the correspondence information between the statistical value and the shade of the color may be displayed, or only one of them may be displayed. Or these correspondence information does not need to be displayed.

  FIG. 8 is a diagram for explaining another example of the fiber display in the first embodiment. For example, the display control unit 17 superimposes and displays an image (for example, a T2-weighted image) that facilitates visual recognition of the brain tumor on the fiber tracking image so that the user can easily recognize the positional relationship with the brain tumor. Also good. For example, the image processing apparatus 10 may acquire a T2-weighted image collected by the MRI apparatus via a network. In FIG. 8, only the brain tumor c is highlighted.

  As described above, according to the first embodiment, since the display mode of each fiber can be changed according to the state information held in the non-DWI volume data, the fiber can be displayed appropriately. For example, according to the first embodiment, since the color can be changed according to the task, the user viewing the fiber can recognize the task type (for example, speech, tapping) and the fiber in association with each other. it can. In addition, for example, according to the first embodiment, since the color shade can be changed according to the statistical value, the user viewing the fiber can recognize how much the fiber is present. it can.

(Modification of the first embodiment)
The processing by the determination unit 16 described above and the display mode definition table can be arbitrarily changed depending on the analysis method of the fMRI analysis and what data is stored as the analysis result. For example, when the fMRI analysis is an analysis for examining the correlation between the signal value change of the image data and the sin curve, the MRI apparatus that executes the fMRI analysis analyzes the correlation coefficient (for example, “0” to “1”). Save as result status information. In this case, the determination unit 16 determines the display mode so that, for example, the fiber passing through the voxel having a high correlation coefficient is displayed in a dark color.

  The difference from the first embodiment is the ascending / descending order relationship and the range. That is, in the first embodiment, when the p value is used, a darker color is assigned as the value is smaller. However, when a correlation coefficient is used, a darker color is assigned as the value is larger. The ascending / descending order is different. Further, the p value and the correlation coefficient have different ranges. A display mode definition table may be prepared for each analysis method so as to cope with such a difference in analysis method, but for example, it is also possible to cope with it as follows.

  First, when storing the analysis result of the fMRI analysis, the method of always storing the value satisfying the relationship “large value = high reliability” can solve the ascending / descending points. . For example, an MRI apparatus that performs fMRI analysis satisfies such a relationship by calculating and saving “1-p” when saving a p value.

  Further, according to the technique of automatically assigning color shades based on the maximum value and the minimum value of the statistical values instead of using the display mode definition table, it is possible to solve the range point. For example, the determination unit 16 assigns color shades based on the maximum value and the minimum value of the statistical values. For example, if the maximum value of the correlation coefficient is “1” and the minimum value is “0”, and the correlation coefficient is “1”, if the RGB = (255, 0, 0), the determination unit 16 determines, RGB of the relation number “0.8” is calculated and assigned as RGB = (255, 204, 204). G and B are calculated as “minimum value“ 0 ”+ 255 × 0.8 / (maximum value“ 1 ”−minimum value“ 0 ”) = 204”.

(Second Embodiment)
Next, the second embodiment will be described. In the first embodiment, an fMRI analysis result is assumed as the non-DWI volume data, and the determination unit 16 determines the color of each fiber according to the task information and the color density according to the statistical value (p value). An example of determining the above has been described. However, the embodiment is not limited to this. In the following description, unless otherwise specified, the image processing apparatus 10 performs the same processing as that of the first embodiment.

  First, as non-DWI volume data, PWI volume data that is an analysis result of PWI (Perfusion Weighted Image) may be used instead of the fMRI analysis result. In this case, the associating unit 15 described in the first embodiment associates the spatial positions of the non-DWI volume data that is PWI volume data and the DWI volume data. The determination unit 16 determines the color shade according to the map value. In this case, the determination unit 16 may compare the map value with a predetermined threshold value and perform fiber tracking using a voxel having a map value exceeding the predetermined threshold value as a starting point. For example, when the value of the map is related to the malignancy of cancer, it is possible to display whether or not the fiber passes through a place where the malignancy of cancer is high. Note that the determination unit 16 may determine the color of each fiber according to the type of map. The map types include, for example, CBF (Cerebral Blood Flow), CBV (Cerebral Blood Volume), MTT (Mean Transit Time), and the like.

  Further, as non-DWI volume data, CSI (Chemical Shift Imaging) volume data which is an analysis result of MRS (Magnetic Resonance Spectroscopy) may be used instead of the fMRI analysis result. In this case, the association unit 15 described in the first embodiment associates the spatial positions of the non-DWI volume data that is CSI volume data and the DWI volume data. Further, the determination unit 16 determines the color shade according to the CSI value. The determination unit 16 may compare the CSI value with a predetermined threshold and perform fiber tracking using a voxel having a CSI value exceeding the predetermined threshold as a starting point. For example, when the value of CSI is related to the malignancy of cancer, it is possible to display whether or not the fiber passes through a place where the malignancy of cancer is high. In addition, the determination part 16 may determine the color of each fiber according to the kind of metabolite.

  Further, as the non-DWI volume data, a T2-weighted image may be used instead of the fMRI analysis result. In this case, the association unit 15 described in the first embodiment associates the spatial positions of the non-DWI volume data that is the T2-weighted image and the DWI volume data. Further, the determination unit 16 determines the shade of the color for displaying each fiber according to the T2 value held in the voxel in the T2-weighted image. Note that the determination unit 16 may compare the T2 value with a predetermined threshold and perform fiber tracking using a voxel having a T2 value exceeding the predetermined threshold as a starting point.

  Note that other anatomical images other than T2 weighting may be used as the non-DWI volume data.

  According to the image processing apparatus of at least one embodiment described above, fibers can be appropriately displayed.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example 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 spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

DESCRIPTION OF SYMBOLS 10 Image processing apparatus 11 DWI volume data memory | storage part 12 Non-DWI volume data memory | storage part 13 Display mode definition memory | storage part 14 Analysis part 15 Corresponding part 16 Determination part 17 Display control part 18 Display part

Claims (10)

An analysis unit that generates DWI (Diffusion Weighted Image) volume data in which the running information of nerve fibers is held in each voxel by tractography analysis,
An association unit that images the same object as the DWI volume data and associates the non-DWI volume data in which the state information of the object is held in each voxel and the spatial position of the DWI volume data;
A determination unit for determining a display mode of nerve fibers passing through the voxels in the DWI volume data corresponding to the voxels according to the state information held in the voxels in the non-DWI volume data;
An image processing apparatus comprising: a display control unit configured to display a nerve fiber derived from the DWI volume data on a display unit according to a display mode determined for each nerve fiber by the determination unit.   The determining unit identifies a plurality of voxels whose spatial positions correspond to a plurality of voxels through which one nerve fiber passes in the DWI volume data among the voxels in the non-DWI volume data. 2. The display state of each nerve fiber is determined according to the derived representative state information when the voxel of the plurality holds state information and derives representative state information. Image processing device.   3. The image processing apparatus according to claim 1, wherein the determination unit determines a shade of a color for displaying each nerve fiber according to a statistical value that is the state information.   The image processing apparatus according to claim 3, wherein the determination unit assigns color shades based on a maximum value and a minimum value of the statistical values. The association unit associates the non-DWI volume data, which is an analysis result obtained by performing a predetermined analysis on MRI volume data collected by an MRI (Magnetic Resonance Imaging) apparatus, and the spatial position of the DWI volume data. With
The image processing apparatus according to claim 1, wherein the determination unit determines a display mode of each nerve fiber according to state information that is an analysis result held in a voxel in the non-DWI volume data. The association unit associates a non-DWI volume data that is an analysis result of f (functional) MRI analysis with a spatial position of the DWI volume data,
The determination unit determines the color of each nerve fiber according to the functional field information held in the voxel in the non-DWI volume data, and determines the color shade according to the reliability of the analysis result. The image processing apparatus according to claim 1, wherein: The association unit associates a non-DWI volume data that is an analysis result of PWI (Perfusion Weighted Image) and a spatial position of the DWI volume data,
The image processing apparatus according to claim 1, wherein the determination unit determines a display mode according to a map value held in a voxel in the non-DWI volume data. The association unit associates non-DWI volume data, which is an analysis result of MRS (Magnetic Resonance Spectroscopy), with a spatial position of the DWI volume data,
The image processing apparatus according to claim 1, wherein the determination unit determines a display mode according to a value of CSI (Chemical Shift Imaging) held in a voxel in the non-DWI volume data. The association unit associates a non-DWI volume data that is a T2-weighted image and a spatial position of the DWI volume data,
The image processing apparatus according to claim 1, wherein the determination unit determines a display mode according to a T2 value held in a voxel in the non-DWI volume data. While displaying nerve fibers derived from DWI (Diffusion Weighted Image) volume data on the display unit in a different display mode for each nerve fiber,
An image processing apparatus comprising: a display control unit that displays, together with the display unit, correspondence information indicating correspondence between each display mode and functional field information that is an analysis result of fMRI analysis.