JP2017051599A - Magnetic resonance imaging apparatus and image processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a magnetic resonance imaging apparatus which can support image analysis regarding a plurality of regions in the brain: and to provide an image processor.SOLUTION: A magnetic resonance imaging apparatus includes a display control part. The display control part performs control so that a matrix regarding a plurality of regions in the brain and showing a connection relation between the regions is displayed. The display control part performs control so that the plurality of regions arranged along a first axis are narrowed down into a part of the regions and are displayed in the matrix on the basis of an attention degree set to each of the plurality of regions.SELECTED DRAWING: Figure 1

Description

  Embodiments described herein relate generally to a magnetic resonance imaging apparatus and an image processing apparatus.

  In recent years, in the field of neuroscience, elucidation regarding the brain functional localized region and the connection between the regions has been advanced using an image captured by a magnetic resonance imaging apparatus. For example, there has been proposed a method for determining the presence or absence of a disease by analyzing a difference from a normal brain regarding a predetermined parameter such as a luminance value of an image for each brain function localized region.

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

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

  A magnetic resonance imaging (MRI) apparatus according to an embodiment includes a display control unit. The display control unit performs control so that a matrix indicating a connection relationship between regions related to a plurality of regions in the brain is displayed. In addition, the display control unit displays a plurality of areas arranged along the first axis in the matrix by narrowing down to a part of the areas based on the degree of attention set in each of the plurality of areas. To control.

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

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

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

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

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

  Here, each gradient magnetic field is superimposed on the static magnetic field generated by the static magnetic field magnet 1 and used to give spatial position information to a magnetic resonance (MR) signal. Specifically, the readout gradient magnetic field gives the MR signal position information along the readout direction by changing the frequency of the MR signal in accordance with the position in the readout direction. Further, the phase encoding gradient magnetic field changes the phase of the MR signal along the phase encoding direction, thereby giving position information in the phase encoding direction to the MR signal. The slice gradient magnetic field is used to determine the direction, thickness, position, and number of slice areas when the imaging area is a slice area, and the position in the slice direction when the imaging area is a volume area. By changing the phase of the MR signal in accordance with the position information, position information along the slice direction is given to the MR signal.

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

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

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

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

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

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

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

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

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

  The processing circuit 12 has a bed control function 12a. Specifically, the couch control function 12 a is connected to the couch 8 and controls the operation of the couch 8 by outputting a control electric signal to the couch 8. For example, the bed control function 12a receives an instruction from the operator to move the top plate 8a in the longitudinal direction, the up-down direction, or the left-right direction via the input circuit 9, and moves the top plate 8a according to the received instruction. The drive mechanism of the top board 8a which the bed 8 has is operated. For example, the processing circuit 12 is realized by a processor.

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

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

  The execution function 13a receives MR signal data from the receiving circuit 7 as a result of executing various pulse sequences, and stores the received MR signal data in the storage circuit 11. The set of MR signal data received by the execution function 13a is arranged two-dimensionally or three-dimensionally according to the position information given by the readout gradient magnetic field, phase encoding gradient magnetic field, and slice gradient magnetic field. Thus, the data is stored in the storage circuit 11 as data constituting the k space.

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

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

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

  Here, the plurality of regions in the brain are obtained by dividing the brain region for some purpose. For example, the plurality of regions in the brain referred to here are functionally or anatomically divided brain regions. Hereinafter, an example in which a plurality of brain function localized regions (also referred to as localized regions) is used as a plurality of regions in the brain will be described, but the embodiment is not limited thereto.

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

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

  Therefore, there is a need for a framework that is easy to understand for general neuroradiologists or physicians and that uses information related to brain function localized areas and connections between the areas according to the purpose of diagnosis.

  In addition, the diagnostic information should be presented in an easy-to-understand manner for general neuro-radiological departments or physicians, and in a mechanism related to a framework that uses information related to brain function localization areas and connections between those areas according to the purpose of diagnosis. Is required.

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

  Specifically, in the present embodiment, the storage circuit 11 stores information representing a matrix indicating connection relationships between regions related to a plurality of localized brain functions. Here, before specifically describing the matrix stored by the storage circuit 11, the hierarchical structure of the brain function localized region, the nerve fiber bundle, and the cortical region will be described.

  FIG. 2 is a diagram for explaining a brain function localized region. For example, as shown in FIG. 2, the brain region is divided into a plurality of brain function localized regions (regions divided by a broken line and a solid line shown in FIG. 2). Here, the brain function localized region includes a cortical region and a white matter region. The cortical region is a cortical region (also called gray matter) composed of nerve cells, and exists so as to cover the surface of the brain. The white matter region is a white matter region (also referred to as subcortical region) that includes nerve fibers, and exists in the brain. Here, the minimum unit of the cortical region is called the gyrus (Gyrus). For example, the cortex is a group of cerebral gyrus, and there are units such as frontal lobe, occipital lobe, temporal lobe, parietal lobe and the like divided into macros.

  FIG. 3 is a diagram for explaining a nerve fiber bundle. A nerve fiber bundle is a collection of nerve fibers. FIG. 3 shows nerve fibers in the cranium connected to the brain function localized region. Here, the nerve fiber (nerve) refers to a bundle of axons generally extending from nerve cells. For example, as shown in FIG. 3, nerve fibers include association fibers (AF) 21, commissural fibers (CF) 22, and projection fibers (PF) 23. The association fiber 21 is a fiber that connects between brain functional localization regions included in the same hemisphere of the brain, and includes a short association fiber and a long association fiber. The commissural fibers 22 are fibers that pass through the corpus callosum and connect between the brain function localized area included in the left hemisphere of the brain and the brain function localized area included in the right hemisphere. The projection fiber 23 is a fiber that connects the cortex region and the white matter region, and is divided into a centrifugal projection fiber that goes from the cortex region to the white matter region and an afferent projection fiber that goes from the white matter region to the cortex region. It is done. Here, the projection fiber 23 is a nerve fiber that connects a brain function localized region and a sensory organ (input) or a motor device (output). A part of the projection fiber 23 is connected to a sensory organ or a motor organ through a thalamus or an internal capsule that is a relay structure.

  These nerve fibers basically pass through some small structures (small functional structures) in the white matter region, such as the thalamus and the internal capsule described above. Therefore, hereinafter, the minimum unit of the brain function localized area including the cortex area and the area of a part of small structures (small function structures) in the white matter area is treated as the gyrus.

  FIG. 4 is a diagram for explaining the hierarchical structure of the cortical region. In general, the gyrus, which is the smallest unit of the cortical region, is divided into a plurality of classifications from a functional or anatomical viewpoint. For example, as shown in FIG. 4, the types of cerebral gyrus, pallidum, putamen, caudate nucleus, substantia nigra, substantia nigra, nucleus accumbens, Myelto basal ganglia, Ammon horn, dentate gyrus, hippocampus In the paratrophies, the entorhinal cortex, the amygdala, and the cingulate gyrus, the pallidum and putamen are classified as the lens nucleus, and the Ammon horn and the dentate gyrus are classified as the hippocampus. Further, the Paleosphere-Myelto basal ganglia are classified as cerebral basal ganglia, and the Ammon horn-zonal gyrus are classified as limbic systems. Thus, the cortical region is represented by a hierarchical structure in which the types of gyrus are classified stepwise.

  FIG. 5 is a diagram showing an example of generalizing the hierarchical structure of the cortical region. In FIG. 5, “g1” to “g20” each represent the type of cerebral gyrus. In addition, “sub-region 1” indicates a small classification of the cerebral gyrus, and “region 1”, “region 2”, and “region 3” indicate an intermediate classification of the cerebral gyrus. “Right hemisphere” and “left hemisphere” are major classifications of the gyrus, “right hemisphere” indicates the right hemisphere of the brain, and “left hemisphere” indicates the left hemisphere of the brain.

  FIG. 5 shows an example in which the gyrus of the cortical region is classified in a hierarchical structure. However, as described above, the gyrus is present not only in the cortical region but also in the white matter region. Therefore, for example, the gyrus of both the cortical region and the white matter region may be classified in a hierarchical structure. In this case, the gyrus of the cortical region and the white matter region are classified based on, for example, the hierarchical structure defined by Talairach et al. (1988). The hierarchical structure of Tairaach is composed of five layers: brain hemisphere, brain lobe, gyrus, tissue, and cell type. Here, the brain hemisphere is the largest classification, and it becomes a small classification step by step in the order of cerebral lobe, gyrus, tissue, cell type, and subcell type.

  FIG. 6 is a diagram showing an example in which the hierarchical structure of the cortical region and the white matter region is generalized. For example, as shown in FIG. 6, the hierarchical structure of the gyrus included in the cortical region and the white matter region includes a hemisphere layer, a lobe layer, a gyrus layer, and a tissue layer. In addition, a sub-cell type layer (sub-cell type) obtained by further subdividing the cell type is added to the five layers of the cell type layer (cell type).

  In FIG. 6, “hemisphere1” indicates a classification included in the hemisphere layer. “Love1” and “love2” indicate classifications included in the cerebral lobe layer. “Gyrus1,” “gyrus2,” and “gyrus3” indicate classifications included in the gyrus. “Gray matter” and “white matter” are classifications included in the tissue layer. “Cell type 1” and “cell type 2” indicate classifications included in the cell type layer. In addition, “g1” to “g14” each represent the type of cerebral gyrus. As shown in FIG. 6, the types of cerebral gyrus represented by “g1” to “g14” do not necessarily have to be classified into the same hierarchy.

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

  In addition, although the example at the time of classifying the gyrus by the hierarchical structure defined by Talairach et al. (1988) has been described here, the embodiment is not limited thereto. For example, the gyrus may be classified based on various brain atlases defined by various companies or various organizations. The generalized hierarchical structure described above can be appropriately changed by subdividing or consolidating according to the definition of the gyrus classification in various brain atlases.

  In this embodiment, the memory circuit 11 divides the nerve fiber bundles that connect between the above-mentioned brain function localized regions and between the brain function localized regions and the organ system according to the function, thereby causing a failure to be diagnosed. Information representing a matrix obtained by dividing a cortical region and a white matter region is stored as a region.

  Specifically, the storage circuit 11 stores information representing a matrix representing the connection relationship between the cortical region and the white matter region for each of the three types of nerve fiber bundles described above. In other words, the storage circuit 11 stores a matrix indicating a connection relationship by associated fibers, a matrix indicating a connection relationship by commissural fibers, and a matrix indicating a connection relationship by projection fibers.

  In the following, the matrix indicating the connection relationship by the associated fibers is referred to as an AFM (Association Fiber Matrix). In addition, a matrix indicating a connection relationship by commissural fibers is referred to as CFM (Commissural Fiber Matrix). A matrix indicating a connection relationship by projection fibers is called a PFM (Projection Fiber Matrix).

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

  For example, as shown in FIG. 7, the AFM arranges cells “g1” to “g20” indicating the gyrus, which is the minimum unit of the cortical region, along the horizontal axis and the vertical axis, respectively, 2 is a matrix in which cells indicating white matter regions including nerve fiber bundles connecting between the vertebral gyrus and the vertical gyrus are two-dimensionally arranged along each axis. In the AFM, for each of the horizontal axis and the vertical axis, a cell indicating a hierarchical structure in which the types of cerebral gyrus are classified stepwise is arranged outside the cell indicating the gyrus.

  Here, in the AFM shown in FIG. 7, the hatched cells are nerve fibers that connect between the gyrus at the corresponding position on the horizontal axis and the gyrus at the corresponding position on the vertical axis. The white matter region containing the bundle is shown. For example, in information representing AFM, “1” is assigned to a cell in a white matter region including a nerve fiber bundle connecting between cerebral gyrus at corresponding positions, and between cerebral gyrus at a corresponding position. “0” is assigned to a cell in a white matter region including a nerve fiber bundle not connected to the cell. In FIG. 7, cells with a lattice pattern indicate that they correspond to the same gyrus assigned to the horizontal axis and the vertical axis, and are treated as invalid cells that do not indicate a connection relationship. .

  For example, the cell 31 shown in FIG. 7 includes a nerve fiber bundle that connects between the gyrus indicated by the cell “g3” arranged on the horizontal axis and the gyrus indicated by the cell “g9” arranged on the vertical axis. The white matter region is shown. In other words, the cell 31 indicates that the gyrus indicated by the cell “g3” arranged on the horizontal axis and the gyrus indicated by the cell “g9” arranged on the vertical axis have a connection relationship. Similarly, the cell 32 shown in FIG. 7 includes a nerve fiber bundle that connects between the gyrus indicated by the cell “g3” arranged on the horizontal axis and the gyrus indicated by the cell “g10” arranged on the vertical axis. The white matter region including In other words, the cell 32 indicates that the gyrus indicated by the cell “g3” arranged on the horizontal axis and the gyrus indicated by “g10” arranged on the vertical axis have a connection relationship. That is, in the example shown in FIG. 7, the gyrus indicated by the cell “g3” arranged on the horizontal axis is the gyrus indicated by the cell “g9” arranged on the vertical axis, and the gyrus indicated by the cell “g10”. Connected to both.

  According to such an AFM, for example, by performing a search using any of the cells “g1” to “g20” arranged along the horizontal axis as a search start position, It is possible to detect white matter regions and gyrus that have a connection relationship with. That is, according to AFM, by performing a search using one of a plurality of cerebral gyrus cells as a search start position, the cerebral gyrus that is connected in the other cerebral gyrus included in the same hemisphere, The white matter region including the nerve fiber bundles connecting the cerebral gyrus can be detected.

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

  For example, as shown in FIG. 9, the CFM arranges cells “g1” to “g20” indicating the gyrus included in the left hemisphere along the horizontal axis, and displays the gyrus included in the right hemisphere along the vertical axis. The cells “g1” to “g20” to be shown are arranged, and cells indicating white matter regions including nerve fiber bundles connecting between the gyrus on the horizontal axis and the gyrus on the vertical axis are two-dimensionally arranged along each axis. Is a matrix arranged in In the CFM, for each of the horizontal axis and the vertical axis, a cell indicating a hierarchical structure in which the types of cerebral gyrus are classified stepwise is arranged outside the cell indicating the gyrus.

  Here, in the CFM shown in FIG. 9, the hatched cells are located at the corresponding gyrus at the corresponding position on the horizontal axis and the corresponding position on the vertical axis, similar to the cells included in the AFM described above. A white matter region including a bundle of nerve fibers connected to a certain gyrus is shown. For example, in information representing CFM, “1” is assigned to a cell in a white matter region including a nerve fiber bundle connecting between cerebral gyrations at corresponding positions, and between the cerebral gyrations at the corresponding position. “0” is assigned to a cell in a white matter region including a nerve fiber bundle not connected to the cell.

  In the AFM shown in FIGS. 7 and 8, cells corresponding to the same cerebral gyrus assigned to the horizontal axis and the vertical axis are treated as invalid cells that do not show a connection relationship. On the other hand, in the CFM shown in FIG. 9, since the vertical axis and the horizontal axis correspond to the left and right hemispheres, cells in the same position as the cells invalidated by the AFM are also included in the left and right hemispheres. It shows the connection relationship of the same type of gyrus.

  For example, the cell 41 shown in FIG. 9 is between the left hemisphere gyrus indicated by the cell “g4” arranged on the horizontal axis and the right gyrus indicated by the cell “g4” arranged on the vertical axis. The white matter region including the nerve fiber bundles to be connected is shown. In other words, in the cell 41, the cerebral gyrus of the left hemisphere indicated by the cell “g4” arranged on the horizontal axis and the cerebral gyrus of the right hemisphere indicated by the cell “g4” arranged on the vertical axis have a connection relationship. Is shown. Similarly, the cell 42 shown in FIG. 9 is between the gyrus of the left hemisphere shown by the cell “g4” arranged on the horizontal axis and the gyrus of the right hemisphere shown by the cell “g11” arranged on the vertical axis. The white matter region including the nerve fiber bundle connecting the two is shown. In other words, the cell 42 has a connection relationship between the gyrus of the left hemisphere indicated by the cell “g4” arranged on the horizontal axis and the gyrus of the right hemisphere indicated by the cell “g11” arranged on the vertical axis. Is shown. That is, in the example shown in FIG. 9, the gyrus of the left hemisphere indicated by the cell “g4” arranged on the horizontal axis is the gyrus of the right hemisphere indicated by the cell “g4” arranged on the vertical axis, and the cell “ It is connected to both gyrus of the right hemisphere indicated by “g11”.

  According to such a CFM, similarly to the AFM, for example, the search start position is set by performing a search using any one of the cells “g1” to “g20” arranged along the horizontal axis as the search start position. The white matter region and the gyrus having a connection relationship with the gyrus indicated by the cell can be detected. In other words, according to the CFM, a search is performed using a cell of the gyrus included in one hemisphere of the left and right hemispheres as a search start position, so that the brains connected in the cerebral gyrus included in the other hemisphere are connected. And white matter regions including nerve fiber bundles connecting the cerebral gyrus can be detected.

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

  For example, as shown in FIG. 10, the PFM arranges cells “g1” to “g20” indicating the gyrus, which is the smallest unit of the cortical region, along the horizontal axis, and further, the cerebral gyrus and organ system on the horizontal axis. It is the matrix which arranged the cell which shows the white matter area | region containing the nerve fiber bundle which connects between them along the horizontal axis. In addition, the organ system here is, for example, a motor organ, a sensory organ system, or the like. That is, the PFM corresponds to a list of organ systems connected to each gyrus. It can be said that this PFM directly reflects clinical symptoms since many clinical symptoms can be said to be some kind of disorder of each of these organs. In the PFM, a cell indicating a hierarchical structure in which the types of gyrus are classified stepwise is arranged below the cell indicating the gyrus.

  Further, as described above, the projection fibers are divided into efferent projection fibers that go from the cortical region to the white matter region and centripetal projection fibers that go from the white matter region to the cortical region. Therefore, cells indicating white matter regions that contain nerve fiber bundles that connect between the gyrus of the horizontal axis and the organ system are also given information indicating whether the nerve fiber bundles are efferent or centripetal. Is done.

  In addition, as described above, the projection fiber is a nerve fiber that connects a brain functional localized region and a sensory organ (input) or a motor device (output). Therefore, information indicating the type of organ system to which the nerve fiber bundle is connected is also given to the cell indicating the white matter region including the nerve fiber bundle that connects between the gyrus on the horizontal axis and the organ system.

  Here, in the PFM shown in FIG. 10, the hatched cells indicate white matter regions including nerve fiber bundles connecting the cerebral gyrus and the organ system at the corresponding positions on the horizontal axis. Yes. For example, in the information representing PFM, when the nerve fiber bundle is efferent, the white matter region cell including the nerve fiber bundle connecting between the gyrus and the organ system at the corresponding position is “ “+1” is given, and “−1” is given in the case of centripetality. In addition, for example, in the information representing PFM, “0” is assigned to a cell in a white matter region including a nerve fiber bundle that is not connected between the gyrus and the organ system at a corresponding position. Furthermore, a label indicating the type of organ system is assigned to a cell in the white matter region connecting the gyrus and the organ system at a corresponding position.

  For example, a cell 51 shown in FIG. 10 shows a white matter region including a nerve fiber bundle that connects between the gyrus and the organ system indicated by the cell “g4” arranged on the horizontal axis. In other words, the cell 51 indicates that the gyrus and the organ system indicated by the cell “g4” arranged on the horizontal axis have a connection relationship.

  For example, “sensor1” to “sensor8” shown in FIG. 10 indicate the types of sensory organs, and “muscle1” to “muscle6” indicate the types of exercise devices, respectively. Examples of the types of sensory organs include eyes, ears, nose, tongue, and skin. The types of exercise equipment include hand muscles, foot muscles, facial expression muscles, and the like. For example, in the example shown in FIG. 10, in the PFM related to the right hemisphere, the gyrus corresponding to the cell “g6” is connected to the sensory organ indicated by “sensor1”, and the gyrus corresponding to “g8” is set to “muscle1”. It is shown that it is connected to the exerciser indicated by “”. In the example shown in FIG. 10, in the PFM related to the left hemisphere, the gyrus corresponding to the cell “g4” is connected to the sensory organ indicated by “sensor5”, and the gyrus corresponding to “g8” is set to “muscle4”. It is shown that it is connected to the exerciser indicated by “”.

  According to such a PFM, for example, by performing a search using any one of the cells “g1” to “g20” arranged along the horizontal axis as a search start position, It is possible to detect white matter regions and organ systems that are connected to each other. In other words, according to the PFM, by performing a search using one of a plurality of cerebral gyrus cells as a search start position, an organ system connected to the cerebral gyrus and a nerve fiber bundle connecting them are included. A white matter region can be detected. Furthermore, it can also be detected whether the nerve fiber bundles contained in the detected white matter region are efferent or centripetal.

  Further, in the present embodiment, the storage circuit 11 stores information representing a matrix in which a degree of attention is set in accordance with a diagnostic purpose and a disease in each of a plurality of brain function localized regions. Hereinafter, such a matrix is referred to as a DSAM (Disease Specific Attention Matrix).

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

  Here, the storage circuit 11 stores information representing the DSAM for each disease for the DSAM for definitive diagnosis. Examples of the disease here include Alzheimer's disease and Parkinson's disease. Further, the storage circuit 11 stores information representing a DSAM that is not limited to a disease for the screening DSAM. That is, since it is required for screening not to specify a disease, a DSAM in which a plurality of diseases are integrated is set. Thus, the storage circuit 11 stores information representing DSAMs having different contents for the definitive diagnosis and the screening.

  FIG. 11 is a diagram illustrating an example of the DSAM according to the first embodiment. For example, as shown in FIG. 11, along the horizontal axis, the DSAM has cells “g1” to “g20” indicating the gyrus, which are the smallest units of the cortical region, and cells “f1” to “g1” indicating the nerve fiber bundles. f8 ”, and a cell in which the cells of interest regarding each gyrus and each nerve fiber bundle on the horizontal axis are arranged above each gyrus and each nerve fiber bundle. In DSAM, cells indicating a hierarchical structure in which the types of gyrus are classified stepwise are arranged outside the cells indicating the gyrus.

  Here, for example, as the degree of attention, the degree of occurrence is set for each localized brain function region that is a frequent occurrence site in a disease. Specifically, in the DSAM for definitive diagnosis, the degree of occurrence is set as the degree of attention for each disease in the localized brain function region that is a common site in each disease. For example, rank values “0” to “4” reflecting the P value obtained from past document information and the like are set as the attention level as follows. In this example, the higher the rank value, the higher the degree of occurrence. That is, the rank value represents the likelihood as an abnormal candidate region of a frequently occurring site.

Rank value “0”: Not focused Rank value “1”: 0.050 ≦ P value <0.100: Reference level Rank value “2”: 0.010 ≦ P value <0.05
Rank value “3”: 0.001 ≦ P value <0.010
Rank value “4”: 0.000 ≦ P value <0.001

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

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

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

  FIG. 12 is a diagram illustrating another example of the DSAM according to the first embodiment. In addition, the example shown in FIG. 12 has shown DSAM regarding a left hemisphere, and abbreviate | omitting illustration about the cell arrange | positioned on a vertical axis | shaft. For example, as shown in FIG. 12, the DSAM may be configured similarly to the AFM. In this case, for example, as shown in FIG. 12, a white matter region including a nerve fiber bundle connecting a cerebral gyrus at a corresponding position on the horizontal axis and a gyrus at a corresponding position on the vertical axis. “F1” to “f8” representing nerve fiber bundles and the degree of attention related to each nerve fiber bundle are set in the cells indicating the cell (cells with diagonal lines).

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

  In the AFM, CFM, PFM, and DSAM, the number of gyrus arranged on the horizontal axis and the vertical axis is not limited to those shown in FIGS. May be changed.

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

  Further, for example, the attention level set in the DSAM may be set based on a reference value other than the P value. That is, the attention degree set in the DSAM may be set based on another statistical value representing the likelihood as the abnormal candidate region of the frequently occurring part.

  In this embodiment, the processing circuit 15 divides the MR image of the subject into brain function localized regions, analyzes the image for each region, refers to the value and DSAM, Image analysis and diagnostic determination are advanced by determining priorities and identifying relevant cortex and white matter from AFM, CFM, and PFM.

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

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

  When the diagnostic purpose and the disease are designated by the operator, the setting function 15a sets imaging conditions for collecting images to be analyzed that are determined in advance according to the diagnostic purpose and the disease. In addition, the setting function 15 a generates sequence execution data for collecting the analysis target image based on the set imaging condition, and transmits the generated sequence execution data to the processing circuit 13. Thereby, MR signal data for generating an image to be analyzed is collected by the execution function 13a of the processing circuit 13. The image to be analyzed is generated by the image generation function 14a of the processing circuit 14 based on the collected MR signal data.

  As the analysis target image here, for example, a morphological image such as a T1 weighted (T1 Weighted) image, a T2 weighted (T2 Weighted) image, a T2 * weighted image, a FLAIR (Fluid Attenuation Inversion Recovery) image, or a magnetization Such as a rate-weighted image, a quantitative susceptibility map (QSM), a diffusion-weighted image, a DTI (Diffusion Tensor Imaging) image, an rs-fMRI (resting state functional MRI) image, or a DTT (Diffusion Tensor Tractography) image Functional images are used.

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

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

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

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

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

  First, the analysis function 15b divides the brain region included in the analysis target image into a plurality of brain function localized regions. At this time, for example, the analysis function 15b performs segmentation of the brain functional localized region using a morphological image captured as one of the analysis images. For example, the analysis function 15b changes a model obtained by dividing a standard brain into a plurality of localized brain function regions according to a morphological image and aligns the model, thereby obtaining a plurality of brain regions depicted in the morphological image. Segment into the region. For example, a T1-weighted image is used as the morphological image here. Then, the analysis function 15b applies each brain function localized region segmented on the morphological image to other images to be analyzed, thereby converting the brain region included in each image into a plurality of brain function localized regions. Divide into

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

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

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

  For example, the specific function 15c indicates the connection relation between the regions related to the plurality of brain function localized regions based on the analysis result regarding the image of the subject and the attention degree set in each of the plurality of brain function localized regions. The search start position of the matrix is specified. Specifically, the specifying function 15c specifies a search start position of AFM, CFM, and PFM using a DSAM in which a degree of attention according to a diagnosis purpose and a disease is set in each of a plurality of brain function localized regions.

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

  The specifying function 15c specifies the search start position using the analysis result of the texture analysis performed by the analyzing function 15b. Further, the specifying function 15c specifies the search start position by further using the analysis result obtained by analyzing the difference from the normal brain related to the predetermined parameter performed by the analysis function 15b. Here, the cell specified as the search start position by the specifying function 15c indicates the gyrus or nerve fiber bundle that is a lesion candidate.

  FIG. 13 is a diagram illustrating an example of specifying the search start position by the specifying function 15c according to the first embodiment. For example, as illustrated in FIG. 13, the specific function 15c includes a plurality of cells “g1” to “g20” based on the DSAM 60, the analysis result 71 of the texture analysis, and the analysis result 72 based on the difference from the normal brain. A cell of the gyrus and a nerve fiber bundle cell as a search start position are specified from “f1” to “f8”. In FIG. 13, only the cells of the gyrus and the cells of interest are shown for the DSAM 60. FIG. 13 illustrates an example in which the gyrus indicated by the cell “g3” is selected as the search start position among the plurality of cerebral gyrus 73 indicated by the plurality of cells “g1” to “g20”.

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

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

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

  For example, the specific function 15c calculates the statistical value of the attention level set in the DSAM 60, the analysis result 71, and the analysis result 72 for each brain time, and starts searching for a cell whose calculated statistical value is equal to or greater than a predetermined threshold value. You may specify as a position. For example, the specifying function 15c calculates an average value, an addition value, a multiplication value, or the like as a statistical value. Further, for example, the specifying function 15c performs weighted addition in which weights are assigned to the attention degrees of the DSAM 60, the analysis result 71, and the analysis result 72 at a predetermined ratio, so that the weighted attention value addition value is used as a statistical value. It may be calculated.

  Further, for example, the specifying function 15c specifies the search start position by using any one or two of the DSAM 60, the analysis result 71, and the analysis result 72 without specifying the search start position. May be. In this case, for example, the specifying function 15c receives any one or two of the DSAM 60, the analysis result 71 of the texture analysis, and the analysis result 72 due to the difference from the normal brain via the input circuit 9. An operation to be selected is received, and the search start position is specified based on the attention level of the item selected by the operator.

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

  Further, for example, the specifying function 15c specifies the search start position based on the analysis result regarding the plurality of types of parameters obtained by the texture analysis and the volume calculation and the analysis result based on the difference from the normal brain regarding the plurality of types of parameters. May be.

  FIG. 14 is a diagram illustrating another example of specifying the search start position by the specifying function 15c according to the first embodiment. For example, as illustrated in FIG. 14, the specific function 15c includes a plurality of cells “g1” to “g1” based on the DSAM 160, the analysis result 171 of the texture analysis and volume calculation, and the analysis result 172 based on the difference from the normal brain. The cell of the gyrus and the nerve fiber bundle cell as the search start position is specified from “g14”. FIG. 14 shows an example in which the DSAM 160 includes cells “g1” to “g14” representing the gyrus. Further, FIG. 14 shows an example in which the analysis result 171 of the texture analysis and volume calculation and the analysis result 172 due to the difference from the normal brain each include an analysis result regarding a plurality of types of parameters (feature 1 to feature n). Yes.

  For example, the specific function 15c calculates an average value of the attention level for each gyrus for a plurality of types of parameters included in the analysis result 171. Then, the specifying function 15c specifies a cell of the gyrus in which the average value of the calculated attention degree exceeds a predetermined threshold value. For example, in the example illustrated in FIG. 13, when the average value of the degree of attention related to the gyrus of the cell “g4” exceeds the threshold, the specifying function 15c specifies the cell “g4” as a search start position candidate. .

  After that, the specifying function 15c refers to the DSAM 160, and whether the average value of the attention level calculated from the analysis result 171 is greater than or equal to the attention level set in the DSAM 160 for the cell specified as the search start position candidate. Determine whether or not. Here, when the average value of the attention level calculated from the analysis result 171 is equal to or higher than the attention level set in the DSAM 160, the specifying function 15c specifies the cell as the search start position. On the other hand, when the average value of the attention level calculated from the analysis result 171 is lower than the attention level set in the DSAM 160, the specifying function 15c excludes the cell from the search start position candidates. For example, in the example illustrated in FIG. 13, when the average value of the attention level calculated from the analysis result 171 is equal to or higher than the attention level “4” set in the DSAM 160 for the cell “g4”, the specific function 15c Specifies the cell “g4” as the search start position.

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

  For example, as illustrated in FIG. 13, the specifying function 15 c generates a list 173 that lists a plurality of cells included in the DSAM 160. In the generated list 173, the specifying function 15c sets the label “1” for the cell specified as the search start position, and sets the label “0” for the cell not specified as the search start position.

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

  Returning to the description of FIG. 1, the search function 15d searches the matrix using the search start position specified by the specification function 15c. Specifically, the search function 15d refers to the information indicating AFM, CFM, and PFM stored in the storage circuit 11, and selects the cell of the gyrus and nerve fiber bundle specified as the search start position by the specifying function 15c. Use to search each matrix.

  FIGS. 15-19 is a figure which shows an example of the search of the matrix by the specific function 15c which concerns on 1st Embodiment. Here, an example will be described in which cell “g3” and cells “f2” and “f4” are specified as search start positions by search function 15d.

  For example, as shown in FIG. 15, the search function 15d starts searching for the cell “g3” specified by the specification function 15c among the cells “g1” to “g20” arranged along the horizontal axis in the AFM 30. Search as a location. Thus, for example, in the example shown in FIG. 15, in the same hemisphere of the brain, the white matter region indicated by the cells 33 and 34 and the cell “ The gyrus indicated by “g9” and “g12” is obtained.

  Further, for example, as illustrated in FIG. 16, the search function 15d selects the cell “g3” specified by the specifying function 15c among the cells “g1” to “g20” arranged along the horizontal axis in the CFM 40. A search is performed as a search start position. Thus, for example, in the example shown in FIG. 16, in the hemisphere on the opposite side of the brain, the white matter region indicated by the cells 43 and 44 as the white matter region and the gyrus having a connection relationship with the gyrus indicated by the cell “g3”; The gyrus indicated by the cells “g3” and “g9” is obtained.

  For example, as illustrated in FIG. 17, the search function 15d selects the cell “g3” specified by the specification function 15c among the cells “g1” to “g20” arranged along the horizontal axis in the PFM 50. A search is performed as a search start position. Thereby, for example, in the example shown in FIG. 17, the white matter region and the organ system indicated by the cell 52 are obtained as the white matter region and the organ system having a connection relation with the gyrus indicated by the cell “g3”. For example, the search function 15d detects a sensory organ indicated by “sensor2” as an organ system having a connection relationship with the gyrus indicated by the cell “g3”. At this time, by referring to the information given to the cell 52, it is also detected whether the nerve fiber bundle included in the identified white matter region is efferent or centripetal.

  Further, for example, as illustrated on the left side of FIG. 18, the search function 15d uses the cell “f2” specified by the specification function 15c among the cells “g1” to “g20” arranged along the horizontal axis in the AFM 30. "Is used as a search start position. Thereby, for example, in the example illustrated in FIG. 18, the cell 33 indicating the white matter region including the nerve fiber bundle indicated by the cell “f2” is specified. As a result, in the same hemisphere of the brain, the gyrus connected by the nerve fiber bundle indicated by the cell “f2” is arranged on the ordinate and the gyrus indicated by the cell “g3” arranged on the horizontal axis. The gyrus indicated by the cell “g9” is obtained.

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

  The specifying function 15c may further search for a matrix using the search result of each matrix. For example, as shown in FIG. 19, the search function 15d searches the AFM 30 as shown in FIG. 15, and as a result, the cell “g9” indicates the cerebral gyrus that is connected to the gyrus indicated by the cell “g3”. After the gyrus is obtained, the PFM 50 is further searched using the searched cell “g9” as the search start position. Thus, for example, in the example shown in FIG. 19, the white matter region and organ system indicated by the cell 53 are obtained as the white matter region and organ system having a connection relationship with the gyrus indicated by the cell “g9”. At this time, by referring to the information given to the cell 53, it is also detected whether the nerve fiber bundle included in the specified white matter region is an efferent nerve fiber bundle or an afferent nerve fiber bundle. The

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

  According to such a configuration, a brain functional localized region having a disorder at the level of the gyrus for a patient with clinical symptoms or a patient candidate who is asymptomatic but has some pathological change. Can be explored.

  For example, “direct search” that searches for localized brain regions linked to projection fibers connected to sensory organs or motor organs that are directly linked to clinical symptoms, with the primary focus on presenting lesion candidates in light of clinical symptoms In addition, it is possible to perform “indirect search” that searches for a vulnerable localized brain function region connected to a localized brain function region (Epi-center) in which a lesion occurs.

  For example, as shown in FIG. 19, when the cerebral gyrus of the cell “g3” is specified as a lesion candidate by the specifying function 15c, the search function 15d sets the cell “g3” as the search start position. By searching for the PFM 50, the organ system connected to the gyrus of the cell “g3” can be specified. Searching for an organ system connected to the gyrus identified as a lesion candidate in this way is called “direct search”.

  On the other hand, when the search function 15d searches the AFM 30, it can be seen that the gyrus of the cell “g9” and the gyrus of the cell “g12” are connected to the gyrus of the cell “g3”. Here, for example, the search function 15d searches the PFM 50 using the cell “g9” as a search start position, whereby the organ system to which the cerebral gyrus of the cell “g9” is connected can be specified. Searching for an organ system connected to another gyrus connected to the gyrus identified as a lesion candidate in this way is called “indirect search”.

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

  In this regard, according to the above configuration, for example, as shown in the example of FIG. 19, even when the gyrus of the cell “g9” is not specified as a lesion candidate because the degree of attention is low, the brain of the cell “g3” By performing an indirect search for the times, the organ system connected to the brain time of the cell “g9” is specified. Therefore, in the actual patient, if it is confirmed that clinical symptoms are appearing in the organ system identified by the indirect search from the cerebral gyrus of the cell “g3”, the cell “g9” is affected by the gyrus of the cell “g3”. It can be estimated that an abnormality has also occurred in the gyrus. Thereby, it is possible to prevent the abnormality of the gyrus of the cell “g9” from being overlooked.

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

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

  In the present embodiment, the display control function 15e performs control so that a matrix indicating a connection relationship between a plurality of regions in the brain is displayed as a matrix search result. Here, the display control function 15e narrows down the plurality of regions arranged along the first axis in the matrix to a partial region based on the attention level set in each of the plurality of regions in the brain. Control to be displayed.

  For example, the display control function 15e displays on the display 10 a matrix that indicates connection relationships between regions related to a plurality of brain function localized regions, as a matrix search result. Here, the display control function 15e uses a plurality of brain function localized areas arranged along the horizontal axis in the matrix based on the attention set for each of the plurality of brain function localized areas. Narrow down to the localized area.

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

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

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

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

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

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

  FIG. 21 is a diagram illustrating an example of a detailed display of a matrix by the display control function 15e according to the first embodiment. For example, as shown in FIG. 21, the display control function 15e displays, for each of the horizontal axis and the vertical axis, a cell indicating a hierarchical structure in which the types of gyrus are classified stepwise outside the cell indicating the gyrus. . For example, the display control function 15e includes, in order from the side closer to the cell of the gyrus, “sr1” cell indicating the subclassification of the gyrus, “r1”, “region2”, and “region3” indicating the midclass of the gyrus. A cell of “left hemisphere” indicating a major classification of the cell and gyrus is displayed.

  At this time, for example, the display control function 15e may display cells indicating a hierarchical structure with abbreviations such as “r1” and “sr1” illustrated in FIG. Here, “r1” is an example of an abbreviation of “region1”, and “sr1” is an example of an abbreviation of “sub-region1”. Further, the display control function 15e may switch display / non-display of information indicating the hierarchical structure in accordance with an instruction from the operator.

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

  In this way, by displaying the matrix with the narrowed down contents, it is possible to make the brain function localized region with a high degree of attention easier to understand. For example, in the case of Alzheimer's disease, it is possible to easily associate a clinical symptom with a brain function localized region by displaying an AFM in which a neural circuit representing a memory function or an emotion function is narrowed down.

  FIG. 22 is a diagram illustrating an example of display of a matrix and an image by the display control function 15e according to the first embodiment. For example, as shown in FIG. 22, the display control function 15 e displays the narrowed matrix 80 on the display 10. Although not shown in FIG. 22, the narrowed matrix 80 is further displayed with cells indicating a hierarchical structure in which the types of cerebral gyrus are classified step by step as shown in FIG. Is done.

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

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

  Further, the display control function 15e enlarges and displays the image of the white matter region connected to the gyrus indicated by the selected cell simultaneously with displaying the image of the selected gyrus. For example, as shown in FIG. 22, when the cell “g8” arranged on the horizontal axis is selected in the matrix 80, the display control function 15e is connected to the two gyrus indicated by the cell “g8”. DTT images 95 and 96 of nerve fiber bundles included in the white matter region are displayed.

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

  In FIG. 22, the display control function 15e includes the MP-RAGE image 91 of the entire brain, the HARDI image 92 of the entire brain, the T2W image 93 of the entire brain, the MPR image 94, the DTT images 95 and 96, and the MPR images 97 and 98. Although an example in which images are displayed side by side has been shown, the image display method is not limited to this. For example, the display control function 15e may switch and display each image in accordance with an instruction from the operator.

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

  Further, the display control function 15e displays an image showing a plurality of brain function localized areas on the display 10, and selects a specific brain function localized area from the brain function localized areas displayed on the image. In accordance with the operation to be selected, the selected brain function localized area among the plurality of brain function localized areas arranged in the matrix 80 is highlighted. For example, the display control function 15e displays the image of the selected gyrus at the same time as displaying the three-dimensional image 99 showing the region of the gyrus connected to the selected gyrus.

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

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

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

  The processing functions of the processing circuit 15 have been described above. Here, for example, each processing function described above is stored in the storage circuit 11 in the form of a program executable by a computer. The processing circuit 15 implements a processing function corresponding to each program by reading each program from the storage circuit 11 and executing each read program. In other words, the processing circuit 15 in a state where each program is read has the processing functions shown in FIG.

  In addition, although the example in case each processing function is implement | achieved by the single processing circuit 15 was demonstrated in FIG. 1, embodiment is not restricted to this. For example, the processing circuit 15 may be configured by combining a plurality of independent processors, and each processing function may be realized by each processor executing each program. In addition, each processing function of the processing circuit 15 may be realized by being appropriately distributed or integrated into a single or a plurality of processing circuits.

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

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

  Subsequently, the analysis function 15b displays the analysis target image generated by the image generation function 14a on the display 10 (step S104). Thereby, it is possible to make the operator confirm whether the generated image has no problem as an analysis target.

  Here, the analysis function 15b is configured to reset the imaging condition when an input indicating that the image to be analyzed is not approved is received from the operator via the input circuit 9 (No in step S105). 15a is controlled. At this time, for example, the setting function 15a accepts an operation for resetting the imaging condition from the operator, and causes the execution function 13a and the image generation function 14a to re-execute imaging based on the reset imaging condition. Then, the analysis function 15b re-displays the captured image on the display 10 as an analysis target image.

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

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

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

  Subsequently, the display control function 15e displays a matrix in which the brain function localized area is narrowed down (step S110). When the operator selects a matrix cell (Yes in step S111), the display control function 15e displays an image corresponding to the selected cell on the display 10 (step S112).

  As described above, in the present embodiment, the likelihood of an abnormal candidate region is specified from the DSAM based on the result of image analysis, and then the nerve fiber bundle and the gyrus as the abnormal candidate region related to the abnormal candidate region are determined. The search result is displayed.

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

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

  As described above, according to the first embodiment, the nerve fiber bundles that are the connection paths between the brain function localized regions and the organ system are divided according to the function, so that the cortex region and the white matter region can be diagnosed as failure regions to be diagnosed. A matrix (AFM, CFM, PFM) is created. Further, a matrix (DSAM) is created in which brain lesion units and values indicating the degree of occurrence of lesions are set in the cortical region and the white matter region, respectively. Here, different contents of DSAM are created for definitive diagnosis and screening.

  Then, after a matrix is set according to the purpose of diagnosis, the brain region included in the subject image is divided into a plurality of brain functional localized regions, and the image for each region is analyzed. Further, the priority of the region to be noted is determined by referring to the analysis value obtained by the analysis and the DSAM. In addition, related gyrus and white matter regions are identified from AFM, CFM, and PFM, and image analysis and diagnosis determination are advanced. In addition, an image obtained by imaging the subject is accumulated, and values related to the frequently occurring site of DSAM are sequentially updated based on comparison with normal brain tissue, thereby improving the accuracy.

  Thereby, it is easy for a general neuroradiologist or physician to understand, and it is possible to provide a framework using information related to a brain function localized region and a connection between the regions according to a diagnostic purpose. As a result, it is possible to enhance the diagnostic ability of a general neuroradiologist or physician, and it is possible to detect abnormal sites efficiently in units of brain functional localized regions.

  In the first embodiment, a matrix in which a cortical region and a white matter region are separated as a failure region to be diagnosed in a nerve fiber bundle divided according to function is presented. At that time, only a region having a high degree of attention in DSAM is presented. Selected and displayed.

  This makes it easy for general neuroradiologists or physicians to present the diagnostic information in an easy-to-understand manner in a framework system that uses information related to brain function localization areas and connections between the areas according to the purpose of diagnosis. Can do. As a result, it is possible to improve the diagnostic ability of a general neuroradiologist or physician, and it is possible to efficiently search for and interpret an abnormal site in units of brain functional localized regions.

  Here, when image analysis is performed in units of brain functional localized regions, it may be considered that image analysis takes time if the brain functional localized regions are subdivided. On the other hand, since the MRI apparatus 100 according to the first embodiment displays the analysis result using a matrix that shows the connection relation between the regions related to the localized brain function prepared in advance, the analysis result is displayed in real time. Can be presented.

  In addition, the real time here is real time in a substantial sense. In other words, the real time here means that the time from when an image is taken until the analysis result is presented is very short. For example, when a patient is taken continuously in a clinical setting, the subject takes an image. It means the time spent in the examination room later, and does not necessarily mean that the time is zero.

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

  In the first embodiment described above, the method for displaying the narrowed-down matrix 80 can be changed as appropriate. For example, the display control function 15e may further display the analysis value of the image regarding each of the plurality of brain function localized regions on the matrix 80.

  25 to 27 are diagrams illustrating another example of the display of the matrix 80 according to the display control function 15e according to the first embodiment. For example, as shown in FIG. 25, the display control function 15e assigns a different color to the analysis value obtained by the image analysis performed by the analysis function 15b according to the magnitude of the analysis value. Then, when displaying the matrix 80, the display control function 15e displays the white matter region cells, which are matrix elements, and the gyrus cells, which are the axis elements of the matrix, by coloring them according to the analysis values.

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

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

  For example, the display control function 15e may further display centrifugal or centripetal information obtained from the PFM on the matrix 80. For example, as shown in FIG. 26, the display control function 15e further displays a cell 81 for displaying the type of centrifugal or centripetality in the vicinity of the cell of the gyrus that is the axis element of the matrix 80. The display control function 15e includes a cell 81 in the vicinity of the gyrus connected to the efferent nerve fiber bundle and a cell 81 in the vicinity of the gyrus connected to the afferent nerve fiber bundle. Display in different colors. Or, for example, as shown in FIG. 27, the display control function 15e is configured such that the gyrus cell frame connected to the efferent nerve fiber bundle and the gyrus connected to the afferent nerve fiber bundle. The cell frame may be displayed in a different color.

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

  FIG. 28 is a diagram illustrating another example of matrix and image display by the display control function 15e according to the first embodiment. For example, as shown in FIG. 28, in addition to the matrix 80 and each image shown in FIG. 22, the display control function 15e displays information 191 and 192 indicating the organ system detected from the PFM by the search function 15d on the display 10. indicate. Note that only one of the information 191 and 192 indicating the organ system may be displayed.

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

(Second Embodiment)
In the first embodiment described above, an example has been described in which the display control function 15e displays a matrix in which brain function localized regions are narrowed down as a search result of AFM, CFM, and PFM. Is not limited to this. For example, the display control function 15e may display a matrix in which brain function localized regions are narrowed down based on DSAM prepared in advance according to predetermined medical information. Below, the example in such a case is demonstrated as 2nd Embodiment.

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

  In the present embodiment, the configuration of the MRI apparatus 150 will be described with a focus on differences from the configuration of the MRI apparatus 100 according to the first embodiment, and plays the same role as the components shown in FIG. The same reference numerals are given to the components, and the detailed description is omitted.

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

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

  In the present embodiment, the storage circuit 211 stores information representing a DSAM in which a degree of attention is set in accordance with predetermined medical information in each of the plurality of brain function localized regions. The predetermined medical information here is, for example, neurological symptoms, diagnostic purposes, diseases, and the like. Moreover, the neurological symptom here is a specific function of the brain such as emotion and memory.

  For example, the medical information may be a brain feedback circuit. For example, in recent studies, correlations between brain functional localization regions where disorders can occur are examined for diseases such as Alzheimer's disease and Parkinson's disease. Such a correlation is called a brain feedback circuit. Therefore, a DSAM that sets a degree of attention according to the feedback circuit may be used.

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

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

  The setting function 215a receives medical information from the operator. Specifically, the setting function 215 a receives an operation for designating medical information from the operator via the input circuit 9. For example, the setting function 215a receives, as medical information, a neurological symptom, a diagnostic purpose, a disease, a type of feedback circuit, and the like.

  When medical information is designated by the operator, the setting function 215a sets an imaging condition for collecting an analysis target image determined in advance according to the medical information. The setting function 215 a generates sequence execution data for collecting the analysis target image based on the set imaging condition, and transmits the generated sequence execution data to the processing circuit 13. Thereby, MR signal data for generating an image to be analyzed is collected by the execution function 13a of the processing circuit 13. The image to be analyzed is generated by the image generation function 14a of the processing circuit 14 based on the collected MR signal data.

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

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

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

  Thereby, for example, the narrowed-down matrix 80 is displayed as shown in FIGS. Also in the present embodiment, the display control function 215e may further display the analysis values of the images relating to each of the plurality of localized brain function regions on the matrix 80 as shown in FIGS.

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

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

  Subsequently, the analysis function 215b displays the analysis target image generated by the image generation function 14a on the display 10 (step S204). Thereby, it is possible to make the operator confirm whether the generated image has no problem as an analysis target.

  Here, the analysis function 215b is configured to reset the imaging condition when an input indicating that the analysis target image is not approved is received from the operator via the input circuit 9 (No in step S205). 215a is controlled. At this time, for example, the setting function 215a accepts an operation for resetting the imaging condition from the operator, and causes the execution function 13a and the image generation function 14a to re-execute imaging based on the reset imaging condition. Then, the analysis function 215b redisplays the captured image on the display 10 as an analysis target image.

  When the analysis function 215b receives an input to approve the analysis target image from the operator via the input circuit 9 (step S205, Yes), the analysis function 215b performs image analysis of the analysis target image. First, the analysis function 215b divides the brain area included in the analysis target image into a plurality of brain function localized areas (step S206). Thereafter, the analysis function 215b performs texture analysis for each divided region, and further analyzes the difference from the normal brain regarding a predetermined parameter (step S207).

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

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

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

  As described above, in the second embodiment, a matrix in which the brain functional localized area is narrowed down is displayed using DSAM prepared according to medical information such as neurological symptoms, diagnostic purpose, disease, and type of feedback circuit. Is done.

  As a result, not only for diagnostic purposes but also for research purposes, etc., it is easy for the operator to understand. It can be presented in an easy-to-understand manner. As a result, an abnormal site can be efficiently searched and interpreted in units of the brain function localized region.

  Therefore, according to the second embodiment, image analysis regarding a plurality of regions in the brain can be supported.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  Similar to the setting function 15a described in the first embodiment, the setting function 351 receives a diagnosis purpose and a diagnosis target disease from the operator.

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

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

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

  The processing functions of the processing circuit 350 have been described above. Here, for example, each processing function described above is stored in the storage circuit 320 in the form of a program executable by a computer. The processing circuit 350 reads each program from the storage circuit 320 and executes each read program, thereby realizing a processing function corresponding to each program. In other words, the processing circuit 350 in a state where each program is read out has each processing function shown in FIG.

  In addition, although the example in case each processing function is implement | achieved by the single processing circuit 350 was demonstrated in FIG. 31, embodiment is not restricted to this. For example, the processing circuit 350 may be configured by combining a plurality of independent processors, and each processor may implement each processing function by executing each program. In addition, each processing function of the processing circuit 350 may be realized by being appropriately distributed or integrated into a single or a plurality of processing circuits.

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

(Fourth embodiment)
FIG. 32 is a diagram illustrating a configuration example of an image processing apparatus 500 according to the fourth embodiment. For example, as illustrated in FIG. 32, the image processing apparatus 500 according to the present embodiment is connected to the MRI apparatus 100 and the image storage apparatus 200 via the network 400. The configurations of the MRI apparatus 100 and the image storage apparatus 200 are the same as those shown in FIG.

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

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

  In the present embodiment, the configuration of the image processing apparatus 500 will be described focusing on differences from the configuration of the image processing apparatus 300 according to the third embodiment, and the same components as those illustrated in FIG. Components that play a role will be given the same reference numerals and will not be described in detail.

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

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

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

  The setting function 551 receives medical information from the operator, like the setting function 215a described in the second embodiment.

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

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

  The processing functions of the processing circuit 550 have been described above. Here, for example, each processing function described above is stored in the storage circuit 520 in the form of a program executable by a computer. The processing circuit 550 implements a processing function corresponding to each program by reading each program from the storage circuit 520 and executing each read program. In other words, the processing circuit 550 that has read each program has each processing function shown in FIG.

  In addition, although the example in case each processing function is implement | achieved by the single processing circuit 550 was demonstrated in FIG. 32, embodiment is not restricted to this. For example, the processing circuit 550 may be configured by combining a plurality of independent processors, and each processor may implement each processing function by executing each program. In addition, each processing function of the processing circuit 550 may be realized by being appropriately distributed or integrated into a single or a plurality of processing circuits.

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

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

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

  Note that the term “processor” used in each of the above-described embodiments is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an application specific integrated circuit (ASIC), or a programmable. Means circuits such as logic devices (for example, Simple Programmable Logic Device (SPLD), Complex Programmable Logic Device (CPLD), and Field Programmable Gate Array (FPGA)) To do. Here, instead of storing the program in the storage circuit, the program may be directly incorporated in the circuit of the processor. In this case, the processor realizes the function by reading and executing the program incorporated in the circuit. In addition, each processor of the present embodiment is not limited to being configured as a single circuit for each processor, but may be configured as a single processor by combining a plurality of independent circuits to realize its function. Good.

  According to at least one embodiment described above, image analysis regarding a plurality of regions in the brain can be supported.

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

Claims (11)

A display control unit that controls to display a matrix indicating a connection relationship between regions related to a plurality of regions in the brain;
The display control unit is configured to display a plurality of regions arranged along the first axis in the matrix by narrowing down to a partial region based on the attention degree set for each of the plurality of regions. To control,
Magnetic resonance imaging device. The display control unit arranges information indicating the partial area along a first axis, and arranges information indicating an area having a connection relationship with the partial area along a second axis. Control so that the displayed matrix is displayed,
The magnetic resonance imaging apparatus according to claim 1. The display control unit is configured to hierarchically display information indicating the area and information indicating a functional or anatomical classification to which the area belongs along the first axis and the second axis. Control,
The magnetic resonance imaging apparatus according to claim 2. The degree of attention is set according to the purpose of diagnosis, disease or neurological symptom,
The magnetic resonance imaging apparatus according to claim 1. The display control unit performs control so that an image of the selected area is displayed according to an operation of selecting the displayed area.
The magnetic resonance imaging apparatus according to claim 1. The display control unit controls the image of the region to be enlarged and displayed;
The magnetic resonance imaging apparatus according to claim 5. The display control unit controls the image of the partial area and the image of the area having a connection relationship with the partial area to be displayed side by side.
The magnetic resonance imaging apparatus according to claim 1. The display control unit performs control so that an image showing the plurality of areas is displayed, and is arranged in the matrix in accordance with an operation of selecting a specific area from the areas displayed on the image. Control the selected area to be highlighted among the multiple areas
The magnetic resonance imaging apparatus according to claim 1. The display control unit controls the analysis values of the images related to the plurality of regions to be further displayed on the matrix;
The magnetic resonance imaging apparatus according to claim 1. The display control unit controls the analysis values displayed on the matrix to be switched according to the type of image to be analyzed.
The magnetic resonance imaging apparatus according to claim 9. A display control unit that controls to display a matrix indicating a connection relationship between regions related to a plurality of regions in the brain;
The display control unit is configured to display a plurality of regions arranged along the first axis in the matrix by narrowing down to a partial region based on the attention degree set for each of the plurality of regions. To control,
Image processing device.