Medical image processing equipment

Abstract

To enable users to easily identify potential placement locations for intravascular electrodes that are close to the source of the nerve activity being measured. [Solution] The medical image processing apparatus according to the embodiment comprises a vessel diameter image generation unit, a distance image generation unit, a device implantation position candidate identification unit, and a display control unit. The vessel diameter image generation unit generates a vessel diameter image representing the size of the vessel diameter inside the brain from a cerebral vessel image taken of the blood vessels of the brain. The distance image generation unit identifies a target position corresponding to the neural activity that is the target of measurement inside the brain, based on an image relating to at least one of the functional areas of the brain and the arrangement of nerves inside the brain, and generates a distance image showing the distance to the identified target position. The device implantation position candidate identification unit identifies a position on the vessel where both the vessel diameter and the distance from the target position satisfy a specified criterion, based on the vessel diameter image and the distance image, as a candidate for device implantation position. The display control unit highlights the identified implantation position candidate on an image representing the brain.

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to medical image processing apparatuses.

Background Art

[0002] As a treatment for compensating the motor function of patients with disorders in motor function due to neurotransmission problems, such as Amyotrophic lateral sclerosis (ALS), there is a Brain Computer Interface (BCI) that directly collects neural activities of the brain to control prosthetic hands and feet.

[0003] For example, as a method for collecting neural activities of the brain, there is a method of placing sheet-like electrodes (also referred to as cortical electrodes) on the brain surface. As a newer method, a technique of collecting nearby neural activities by placing stent-like electrodes (also referred to as endovascular electrodes) into blood vessels using a catheter has been studied. With the technique of endovascular electrodes, electrodes can be installed in the skull by a safe method of placing a device in blood vessels that have already been established in the circulatory region. This has the advantage that electrodes can be placed in the skull with less invasiveness than the conventional method of placing cortical electrodes on the brain surface by craniotomy. Endovascular electrodes measure the potential difference generated by neural activities of the brain as it is transmitted through the surrounding brain tissue and blood vessel tissue, so the sensitivity is lower compared to cortical electrodes. In order to suppress the decrease in sensitivity, the endovascular electrodes should be placed as close as possible to the location where the neural activities of the measurement target occur. Also, since catheter insertion into thin blood vessels is dangerous, it is necessary to select blood vessels thick enough to place the endovascular electrodes.

[0004] However, since the blood vessel network surrounding the brain has a complex shape and its inner diameter also varies, it has not been easy for the user to determine the placement position of the endovascular electrodes considering both which blood vessels are close to the measurement target and which blood vessels allow catheter insertion.

Prior Art Documents

Non-Patent Documents

[0005] [Non-Patent Document 1] Thomas J Oxley, Peter E Yoo, Gil S Rind, Stephen M Ronayne, CM Sarah Lee, Christin Bird, Victoria Hampshire, Rahul P Sharma, Andrew Morokoff, Daryl L Williams, Christopher MacIsaac, Mark E Howard, Lou Irving, Ivan Vrljic, Cameron Williams, Sam E John, Frank Weissenborn, Madeleine Dazenko, Anna H Balabanski, David Friedenberg, Anthony N Burkitt, Yan T Wong, Katharine J Drummond, Patricia Desmond, Douglas Weber, Timothy Denison, Leigh R Hochberg, Susan Mathers, Terence JO'Brien, Clive N May, J Mocco, David B Grayden, Bruce C v Campbell, Peter Mitchell, Nicholas L Opie, Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks IN severe paralysis: first IN-human "Experience," Journal of NeuroInterventional Surgery, Volume 13-2, October 28, 2020, Internet “https: / / jnis.bmj.com / content / 13 / 2 / 102” [Overview of the project] [Problems that the invention aims to solve]

[0006] One of the problems that the embodiments disclosed herein and in the drawings aim to solve is to enable users to easily identify candidate placement locations for intravascular electrodes that can be placed near the site of neural activity in the brain that is the target of measurement. However, the problems that the embodiments disclosed herein and in the drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0007] The medical image processing apparatus according to the embodiment comprises a vessel diameter image generation unit, a distance image generation unit, a device implantation position candidate identification unit, and a display control unit. The vessel diameter image generation unit generates a vessel diameter image representing the size of the vessel diameter inside the brain from a cerebral vessel image obtained by photographing the blood vessels of the brain. The distance image generation unit identifies a target position corresponding to the neural activity to be measured inside the brain, based on an image relating to at least one of the functional regions of the brain and the arrangement of nerves inside the brain, and generates a distance image showing the distance to the identified target position. The device implantation position candidate identification unit identifies a position on the vessel where both the vessel diameter and the distance from the target position satisfy a predetermined criterion, based on the vessel diameter image and the distance image, as a candidate for device implantation position. The display control unit highlights the identified implantation position candidate on an image representing the brain. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a diagram illustrating the overview of the input and output of the processing of a medical image processing apparatus according to the first embodiment. [Figure 2] Figure 2 shows an example of the configuration of a medical image processing apparatus according to the first embodiment. [Figure 3] Figure 3 shows an example of a cerebral vascular image and a vascular diameter image according to the first embodiment. [Figure 4] Figure 4 shows an example of a brain function image and a brain function distance image according to the first embodiment. [Figure 5]Figure 5 shows an example of a brain functional image and a brain functional distance image having two activated regions according to the first embodiment. [Figure 6] Figure 6 shows an example of a nerve fiber map and nerve fiber endpoint distance image according to the first embodiment. [Figure 7] Figure 7 shows an example of an image indicating a candidate implantation location using a cerebral vascular image according to the first embodiment. [Figure 8] Figure 8 shows an example of an image indicating a candidate implantation location using a blood vessel diameter image according to the first embodiment. [Figure 9] Figure 9 shows an example of an image indicating a candidate implantation location using brain function images according to the first embodiment. [Figure 10] Figure 10 shows an example of an implantation candidate position display image using nerve fiber images according to the first embodiment. [Figure 11] Figure 11 is a flowchart showing an example of the process for identifying candidate implantation locations for intravascular electrodes according to the first embodiment. [Figure 12] Figure 12 is a diagram illustrating the overview of the input and output of the processing of a medical image processing apparatus according to the second embodiment. [Figure 13] Figure 13 shows an example of the configuration of a medical image processing apparatus according to the second embodiment. [Figure 14] Figure 14 is a flowchart showing an example of the process for identifying candidate implantation locations for intravascular electrodes according to the second embodiment. [Figure 15] Figure 15 is a diagram illustrating the overview of the input and output of the processing of a medical image processing apparatus according to the third embodiment. [Figure 16] Figure 16 shows an example of the configuration of a medical image processing apparatus according to the third embodiment. [Figure 17] Figure 17 is a flowchart showing an example of the process for identifying candidate implantation locations for intravascular electrodes according to the third embodiment. [Figure 18] Figure 18 is a diagram illustrating the overview of the input and output of the processing of a medical image processing apparatus according to the fourth embodiment. [Figure 19]FIG. 19 is a diagram showing an example of the configuration of a medical image processing apparatus according to the fourth embodiment. [Figure 20] FIG. 20 is a diagram showing an example of a brain region atlas and a brain region distance image according to the fourth embodiment. [Figure 21] FIG. 21 is a flowchart showing an example of the flow of a process for specifying a candidate placement position of an intravascular electrode according to the fourth embodiment.

MODE FOR CARRYING OUT THE INVENTION

[0009] (First Embodiment) Hereinafter, embodiments of a medical image processing apparatus will be described in detail with reference to the drawings. The medical image processing apparatus of the present embodiment is used when positioning the placement of an intravascular electrode. The intravascular electrode is a stent-shaped electrode and is inserted percutaneously with a catheter and placed in the blood vessels of the brain of a subject (patient) in order to collect the neural activity of the brain. The intravascular electrode measures the potential difference generated by the neural activity of the brain that has been transmitted through the surrounding brain tissue and blood vessel tissue. Therefore, it becomes possible to collect the neural activity in the vicinity of the position where the intravascular electrode is placed. The intravascular electrode is an example of a device in the present embodiment.

[0010] FIG. 1 is a diagram for explaining an overview of the input and output of the processing of a medical image processing apparatus according to the first embodiment. As shown in FIG. 1, the medical image processing apparatus of the present embodiment takes a cerebral blood vessel image 910, a brain function image 920, and a nerve fiber image 930 as inputs and outputs a candidate placement position display image 900.

[0011] The cerebrovascular image 910 is an image in which the blood vessels of the brain of a subject, which is the measurement target of neural activity, are visualized. The blood vessels of the subject's brain are the target for the placement of intravascular electrodes for the measurement of the neural activity. As the cerebrovascular image 910, various medical images can be adopted. For example, when a doctor places an intravascular electrode in an artery, an image taken by CT (Computed Tomography) Angiography, MRI (Magnetic Resonance Imaging) Angiography, DSA (Digital Subtraction Angiography), or the like is used as the cerebrovascular image 910. Also, when a doctor places an intravascular electrode in a vein, an image taken by MR Venography or DSA is used as the cerebrovascular image 910.

[0012] The brain function image 920 is an image in which the activation region of the neural activity to be measured by the intravascular electrode is visualized. For example, when the neural activity to be measured occurs during the movement of the fingers or ankles of the subject, the brain function image 920 is a functional MRI (Magnetic Resonance Imaging) (fMRI) image or the like when the subject is performing a flexion movement task of the fingers or ankles. Also, when the neural activity to be measured occurs when the subject consciously imagines moving the fingers or ankles, the brain function image 920 is a functional MRI (Magnetic Resonance Imaging) (fMRI) image or the like when the subject is consciously imagining moving the fingers or ankles. The brain function image 920 is taken while the subject is performing an action or imagining related to the neural activity to be measured.

[0013]

[0014] ​The implantation candidate position display image 900 is an image representing the brain that highlights potential implantation locations for intravascular electrodes. Physicians and other medical professionals confirm the implantation candidate position display image 900 to determine the placement location for the intravascular electrodes. The potential implantation locations for intravascular electrodes are within the vascular region of the brain, with a vessel diameter large enough to accommodate the electrode, and are close to the activated area of ​​the brain function being measured and the endpoints of nerve fibers. The method for identifying suitable implantation locations for intravascular electrodes will be described later.

[0015] In this embodiment, the cerebral blood vessel image 910, brain function image 920, nerve fiber image 930, and implantation candidate position display image 900 are, for example, three-dimensional image data (volume data). However, some or all of these may be two-dimensional image data.

[0016] Figure 2 shows an example of the configuration of the medical image processing apparatus 100a according to the first embodiment. The medical image processing apparatus 100a is, for example, a computer such as a server or a PC (Personal Computer).

[0017] As shown in Figure 2, the medical image processing device 100a includes, for example, a network interface 110, a memory circuit 120, an input interface 130, a display 140, and a processing circuit 150.

[0018] The NW interface 110 is connected to the processing circuit 150 and controls the transmission and communication of various data between the medical image processing device 100a and other devices. Other devices include, but are not limited to, medical image storage devices such as PACS (Picture Archiving and Communication System) for storing medical image data, various modalities (medical imaging devices), and electronic medical record systems. The NW interface 110 is implemented by a network card, network adapter, NIC (Network Interface Controller), etc.

[0019] The memory circuit 120 stores various types of information used by the processing circuit 150 in advance. The memory circuit 120 also stores various programs. The memory circuit 120 is, for example, a non-volatile storage device such as an HDD (Hard Disk Drive), SSD (Solid State Drive), or integrated circuit memory device that stores various types of information. In addition to HDDs and SSDs, the memory circuit 120 may also be a drive device that reads and writes various types of information to portable storage media such as CDs (Compact Discs), DVDs (Digital Versatile Discs), flash memory, or semiconductor memory elements such as RAM (Random Access Memory). The memory circuit 120 is an example of a storage unit.

[0020] The input interface 130 is implemented by a mouse, keyboard, pen tablet (combining a stylus and tablet that accept user input), trackball, switch buttons, touchpad (for input operations by touching the operating surface), touchscreen (integrating a display screen and touchpad), non-contact input circuit using an optical sensor, and audio input circuit, etc. The input interface 130 may include multiple devices that accept user operations. The input interface 130 is connected to the processing circuit 150 and converts the input operations received from the user into electrical signals and outputs them to the processing circuit 150. In this specification, the input interface is not limited to those equipped with physical operating components such as a mouse or keyboard. For example, an electrical signal processing circuit that receives electrical signals corresponding to input operations from an external input device provided separately from the device and outputs these electrical signals to the processing circuit 150 is also included as an example of an input interface.

[0021] The display 140 displays various information under the control of the processing circuit 150. For example, the display 140 outputs the generated implantation candidate position display image 900 and a GUI (Graphical User Interface) for accepting various operations from the user. Specifically, the display 140 is a liquid crystal display or a CRT (Cathode Ray Tube) display, etc. The input interface 130 and the display 140 may be integrated. For example, the input interface 130 and the display 140 may be implemented by a touch panel. The display 140 is an example of a display unit.

[0022] The processing circuit 150 is a processor that reads programs from the memory circuit 120 and executes them to realize functions corresponding to each program. The processing circuit 150 in this embodiment includes a reception function 151, an acquisition function 152, a blood vessel diameter image generation function 153, a brain distance image generation function 154, a nerve fiber endpoint distance image generation function 155, a specification function 156, and a display control function 157. The reception function 151 is an example of a reception unit. The acquisition function 152 is an example of an acquisition unit. The blood vessel diameter image generation function 153 is an example of a blood vessel diameter image generation unit. The brain distance image generation function 154 and the nerve fiber endpoint distance image generation function 155 are examples of distance image generation units. The brain distance image generation function 154 may also be an example of a brain distance image generation unit. The nerve fiber endpoint distance image generation function 155 may also be an example of a nerve fiber endpoint distance image generation unit. The specification function 156 is an example of a device implantation position candidate identification unit. The display control function 157 is an example of a display control unit.

[0023] Here, for example, the processing functions of the processing circuit 150, which are components of the processing circuit 150, such as the reception function 151, acquisition function 152, blood vessel diameter image generation function 153, brain distance image generation function 154, nerve fiber endpoint distance image generation function 155, identification function 156, and display control function 157, are stored in the memory circuit 120 in the form of programs that can be executed by a computer. The processing circuit 150 is a processor. For example, the processing circuit 150 reads the program from the memory circuit 120 and executes it to realize the function corresponding to each program. In other words, the processing circuit 150 in the state in which each program has been read will have the functions shown in the processing circuit 150 in Figure 2. In Figure 2, the processing functions performed by the reception function 151, acquisition function 152, blood vessel diameter image generation function 153, brain distance image generation function 154, nerve fiber endpoint distance image generation function 155, identification function 156, and display control function 157 are explained as being realized by a single processor. However, it is also acceptable to configure the processing circuit 150 by combining multiple independent processors, with each processor executing a program to realize the functions. Furthermore, in Figure 2, the processing circuit 150 is explained as storing programs corresponding to each processing function, but it is also acceptable to distribute multiple memory circuits and configure the processing circuit 150 to read the corresponding programs from individual memory circuits.

[0024] The above description illustrates an example in which a "processor" reads and executes programs corresponding to each function from a memory circuit, but the embodiments are not limited to this. The term "processor" refers to circuits such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), Application Specific Integrated Circuit (ASIC), and Programmable Logic Device (e.g., Simple Programmable Logic Device (SPLD), Complex Programmable Logic Device (CPLD), and Field Programmable Gate Array (FPGA)). If the processor is a CPU, for example, it realizes its functions by reading and executing programs stored in a memory circuit. On the other hand, if the processor is an ASIC, instead of storing the program in the memory circuit 120, the function is directly incorporated as a logic circuit within the processor's circuit. In this embodiment, each processor is not limited to being configured as a single circuit; multiple independent circuits may be combined to form a single processor and realize its functions. Furthermore, the multiple components shown in Figure 2 may be integrated into a single processor to realize its functions.

[0025] The reception function 151 accepts various user operations via the input interface 130. For example, when a physician or other medical professional confirms the implantation candidate position display image 900 and determines the implantation position of an intravascular electrode, the reception function 151 accepts the operation by the physician or other medical professional to determine the implantation candidate position display image 900. The reception function 151 may also accept user operations to change the implantation candidate position on the implantation candidate position display image 900. Furthermore, the reception function 151 may accept user operations to change the prescribed criteria for identifying the implantation candidate position. The prescribed criteria for identifying the implantation candidate position will be described later. In addition, if there are two or more activation regions 80 in the subject's brain at the same time, the reception function 151 may accept user operations to select which activation region 80 to measure.

[0026] Furthermore, the reception function 151 may accept user input to select the type of background image for the candidate placement location display image 900 and the type of text information to be displayed along with the candidate placement location display image 900. The types of background images for the candidate placement location display image 900 and the text information to be displayed will be described later.

[0027] The acquisition function 152 acquires medical images to be used for generating implantation candidate position display images 900, for example, via the NW interface 110. The source of the medical images may be the modality that captured each medical image, or a medical image storage device that stores each medical image.

[0028] More specifically, the acquisition function 152 acquires a cerebral blood vessel image 910 and an image relating to at least one of the functional regions of the brain and the arrangement of nerves within the brain. In this embodiment, the acquisition function 152 acquires a brain function image 920 as an image relating to the functional regions of the brain. The acquisition function 152 also acquires a nerve fiber image 930 as an image relating to the arrangement of nerves within the brain.

[0029] The cerebral vascular images 910, brain function images 920, and nerve fiber images 930 acquired by the acquisition function 152 are images taken of the brain of the same subject.

[0030] The blood vessel diameter image generation function 153 generates blood vessel diameter images from the cerebral blood vessel images 910. Blood vessel diameter images represent the size of the blood vessels in the brain.

[0031] Figure 3 shows an example of a cerebral vascular image 910 and a vessel diameter image 911 according to the first embodiment. In the example shown in Figure 3, the size of the vessel diameter in the cerebral vascular image 910 is represented, for example, by pixel values ​​(shading). The cerebral vascular image 910 contains a vessel region and a background region that can be distinguished by pixel values. The vessel diameter image generation function 153 calculates the vessel diameter by determining the distance from the center line of the vessel region included in the cerebral vascular image 910 in the normal direction to the outer edge of the vessel region.

[0032] Furthermore, the vessel diameter image generation function 153 can determine the centerline of the vessel region by binarizing and thinning the cerebral vessel image 910. In addition, the vessel diameter image generation function 153 can determine the outer edge of the vessel region by binarizing and edge filtering the cerebral vessel image 910.

[0033] The blood vessel diameter image generation function 153 embeds the blood vessel diameter at that location (the position of each pixel on the centerline) as a pixel value into each pixel on the centerline of the blood vessel region of the cerebral blood vessel image 910. As a result, the blood vessel diameter image generation function 153 generates a blood vessel diameter image 911 in which the thickness of each blood vessel at each location is represented by pixel values ​​(shading). For example, the blood vessel diameter image generation function 153 generates the blood vessel diameter image 911 such that the pixel value becomes smaller (the color becomes darker) as the blood vessel diameter increases within the blood vessel region.

[0034] Furthermore, the method for generating the vessel diameter image does not have to be the method described above. For example, a machine learning model trained on a pre-prepared dataset of cerebral vessel images and corresponding vessel diameter images may be used. In this case, the vessel diameter image generation function 153 may input the cerebral vessel image 910 into the model and obtain the vessel diameter image 911 as the output of the model.

[0035] Returning to Figure 2, the brain distance image generation function 154 generates a brain functional distance image from the brain functional image 920. The brain functional distance image is an example of a distance image in this embodiment. The brain functional distance image is an image that represents the distance from the activation region to each pixel. The activation region is an example of a target location in this embodiment.

[0036] Figure 4 shows an example of a brain function image 920a and a brain function distance image 921 according to the first embodiment. In the example shown in Figure 4, one activated region 80 is depicted in the brain function image 920a. The brain distance image generation function 154 identifies the activated region 80 in the brain function image 920a. The brain distance image generation function 154 then generates a brain function distance image 921 that represents the distance from the identified activated region in the brain tissue.

[0037] More specifically, let's assume, for example, that the brain functional image 920a is an fMRI image. In this case, the brain functional image 920a represents the strength of the difference in signal strength at locations where there was a certain difference in signal between tasked and untasked periods during fMRI imaging, with the strength of that difference being used as the pixel value. Generally, most brain regions have the same signal whether a task is being performed or not, so as shown in Figure 4, the brain functional image 920a will have pixel values ​​only in a portion of the brain (activation region 80). Therefore, the brain distance image generation function 154 can identify the regions with pixel values ​​in the brain functional image 920a as the activation region 80.

[0038] The brain distance image generation function 154 then generates a brain functional distance image 921 by using the distance from the activation region 80 to each pixel as the pixel value for all pixels in the brain functional image 920a. In other words, the brain functional distance image 921 is an image in which the distance from the activation region 80 to the position of each pixel is represented as a pixel value (grayscale).

[0039] In Figure 4, the brain distance image generation function 154 sets the pixel values ​​for each pixel corresponding to the brain region depicted in the brain functional distance image 921 such that the pixel value becomes smaller (the color becomes darker) as the distance from the activated region 80 increases. Alternatively, the brain distance image generation function 154 may set the pixel values ​​such that the pixel value becomes smaller (the color becomes darker) as the distance from the activated region 80 increases.

[0040] Note that in Figure 4, dashed lines are shown like contour lines to indicate the range from the activated region 80 in the brain function distance image 921. However, this is a representation that emphasizes the boundaries of pixel values ​​(shade) for illustrative purposes, and the dashed lines do not actually need to be displayed.

[0041] Furthermore, in some cases, two or more activated regions 80 may occur simultaneously in the subject's brain. If there are multiple activated regions 80, the brain distance image generation function 154 generates a brain functional distance image 921 for each activated region 80.

[0042] Figure 5 shows an example of a brain function image 920b and brain function distance images 921a, 921b having two activation regions 80a, 80b according to the first embodiment. Brain function distance image 921a (brain function distance image 1) is an image representing the distance from the activation region 80a to each pixel. Brain function distance image 921b (brain function distance image 2) is an image representing the distance from the activation region 80b to each pixel.

[0043] Furthermore, if either of the two activation regions 80a and 80b is the target of measurement, the user may select one of several brain function distance images 921a and 921b before the generation of the implantation candidate position display image 900.

[0044] The brain functional images 920a and 920b shown in Figures 4 and 5 are examples of brain functional images 920. Hereafter, unless there is a specific intention to limit the number of activated regions, the term "brain functional image 920" will simply be used.

[0045] Furthermore, the method for generating the brain function distance image 921 is not limited to the method described above. For example, the brain distance image generation function 154 may use a machine learning model that has been trained on a pre-prepared dataset of brain function images with activated regions and corresponding brain function distance images. In this case, the brain distance image generation function 154 may input the brain function image 920 into the model and obtain the brain function distance image 921 as the output of the model.

[0046] Returning to Figure 2, the nerve fiber endpoint distance image generation function 155 generates a nerve fiber endpoint distance image from the nerve fiber image 930. The nerve fiber endpoint distance image is another example of a distance image in this embodiment. The nerve fiber endpoint distance image is an image that represents the distance from the endpoint of the nerve fiber to be measured. The endpoint of the nerve fiber to be measured is the endpoint of a nerve fiber in which neural activity is activated in the brain. In other words, the endpoint of the nerve fiber to be measured is the endpoint of a nerve fiber that corresponds to the activated region 80 among the nerve fibers contained in the brain. The endpoints of nerve fibers that can be seen in MRI tractography are assumed to be densely populated with nerve cell bodies from a neurophysiological perspective and are important as targets for measuring neural activity by intravascular electrodes. The endpoint of the nerve fiber to be measured is another example of a target location in this embodiment. The activated region identified from the brain function image 920 and the endpoint of the target nerve fiber identified from the nerve fiber image 930 are basically in the same or close positions. In other words, the location of the nerve activity being measured can be identified based on either the activated region identified from the brain function image 920 or the endpoint of the target nerve fiber identified from the nerve fiber image 930.

[0047] Figure 6 shows an example of a nerve fiber image 930 and a nerve fiber endpoint distance image 931 according to the first embodiment. The nerve fiber endpoint distance image generation function 155 generates the nerve fiber endpoint distance image 931 by setting the distance from the endpoints (nerve fiber endpoints) 71a, 71b of the nerve fibers 70a, 70b to each pixel as the pixel value for all pixels of the nerve fiber image 930. In other words, the nerve fiber endpoint distance image 931 is an image in which the distance from the nerve fiber endpoints 71a, 71b to the position of each pixel is represented as a pixel value (grayscale).

[0048] As shown in Figure 6, since multiple nerve fibers 70a, 70b are generally bundled together, the nerve fiber endpoint distance image generation function 155 may use the nerve fiber endpoints 71a, 71b of the nerve fibers 70a, 70b included in one bundle as a single unit and use them as the reference point for calculating the distance.

[0049] In Figure 6, the nerve fiber endpoint distance image generation function 155 sets the pixel values ​​for each pixel corresponding to the brain region depicted in the nerve fiber endpoint distance image 931 such that the pixel value becomes smaller (the color becomes darker) as the distance from the nerve fiber endpoints 71a and 71b increases. Alternatively, the nerve fiber endpoint distance image generation function 155 may set the pixel values ​​such that the pixel value becomes smaller (the color becomes darker) as the distance from the nerve fiber endpoints 71a and 71b increases.

[0050] Note that in Figure 6, dashed lines are shown to indicate the range of the same distance from nerve fiber endpoints 71a and 71b in the nerve fiber endpoint distance image 931. However, this is an illustrative representation that emphasizes the boundaries of pixel values ​​(shading), and the dashed lines do not actually need to be displayed. Also, as explained in Figure 5, there may be multiple regions in the brain where neural activity is activated simultaneously. In this case, the endpoints of nerve fibers related to different bundles of nerve fibers located at different positions may be the target of measurement. In this case, the nerve fiber endpoint distance image generation function 155 may generate a nerve fiber endpoint distance image 931 showing the distance to the nerve fiber endpoint for each of the activated bundles of nerve fibers.

[0051] Returning to Figure 2, the specific function 156 identifies locations within the cerebral vascular region where both the vascular diameter and the distance from the target location meet specified criteria, based on the vascular diameter image 911, the brain functional distance image 921, and the nerve fiber endpoint distance image 931, as candidate locations for device implantation.

[0052] The standard for the vessel diameter is that the vessel diameter is greater than or equal to a specified size. The specified size of the vessel diameter depends on the diameter of the catheter used for device placement. The catheter diameter varies depending on the product, but is typically around 0.5 mm to 2 mm. The diameter of the catheter to be used in the procedure for placing intravascular electrodes may be stored in, for example, the memory circuit 120. In this case, the specified size of the vessel diameter will be, for example, the catheter diameter stored in the memory circuit 120 plus a buffer size. The buffer value (e.g., 0.3 mm) may be predetermined and stored in the memory circuit 120, etc.

[0053] The specified criterion regarding the distance from the target location is that the distance from the activation area 80 and the nerve fiber endpoints 71a and 71b to be measured is less than or equal to the specified distance. The specified distance is, for example, the distance at which the intravascular electrode can measure signals caused by nerve activity. The specific function 156 identifies the location with the shortest distance from the target location as a candidate implantation site if, among blood vessels with a diameter equal to or greater than the specified size, that location satisfies the specified criterion regarding the distance from the target location. Furthermore, if there are two or more locations with the shortest distance from the target location, the specific function 156 may identify multiple candidate implantation sites.

[0054] To further explain the method for identifying candidate implantation locations, the identification function 156 aligns the blood vessel diameter image 911, the brain function distance image 921, and the nerve fiber endpoint distance image 931 on a pixel-by-pixel basis. Known techniques can be used for aligning the images. As described above, the pixel values ​​of the blood vessel diameter image 911 indicate the size of the blood vessel diameter. The pixel values ​​of the brain function distance image 921 and the nerve fiber endpoint distance image 931 represent the distance from the target location. Therefore, the identification function 156 can identify the distance between the blood vessel diameter and the target location at each pixel based on the pixel values ​​of the blood vessel diameter image 911, the brain function distance image 921, and the nerve fiber endpoint distance image 931. The identification function 156 identifies a pixel as a candidate implantation location if, among the pixels where the blood vessel diameter is greater than or equal to a specified size, the pixel with the smallest distance from the target location is less than or equal to the specified distance from the target location.

[0055] The display control function 157 highlights the implantation site candidates identified by the identification function 156 on the image representing the subject's brain. The image in which the implantation site candidates are superimposed on the image representing the subject's brain is called the implantation site candidate display image 900. The display control function 157 displays the implantation site candidate display image 900 on the display 140.

[0056] The images representing the subject's brain used in the implantation candidate position display image 900 are, for example, cerebral vascular images 910, brain function images 920, nerve fiber images 930, vascular diameter images 911, brain function distance images 921, or nerve fiber endpoint distance images 931, but are not limited to these.

[0057] Furthermore, the display control function 157 may display characters representing the blood vessel diameter at the candidate implantation location, or the distance to the target location, on the image representing the brain.

[0058] Figures 7 to 10 show variations in the display mode of the implantation candidate position display image 900.

[0059] Figure 7 shows an example of an implantation candidate location display image 900a using the cerebral vascular image 910 according to the first embodiment. In the implantation candidate location display image 900a, a figure 60 indicating the implantation candidate location is superimposed on the cerebral vascular image 910. This display allows the user, such as a physician, to understand the location of the implantation candidate location in the subject's brain and the vascular pathways leading to the implantation candidate location.

[0060] Figure 8 shows an example of a candidate implantation location display image 900b using the blood vessel diameter image 911 according to the first embodiment. In the candidate implantation location display image 900b, a figure 60 indicating the candidate implantation location is superimposed on the blood vessel diameter image 911. With this display, users such as physicians can easily grasp the location of the candidate implantation location in the subject's brain, as well as the blood vessel diameter of the blood vessels in the brain, including the blood vessels that reach the candidate implantation location.

[0061] Furthermore, in Figure 8, the display control function 157 displays the text "Vascular diameter: 4mm" indicating the vessel diameter at the implantation candidate location on the implantation candidate location display image 900b. The display control function 157 displays this text in a display area 61a, for example, that does not overlap with the figure 60 indicating the implantation candidate location on the vessel diameter image 911.

[0062] Figure 9 shows an example of an implantation candidate position display image 900c using a brain function image 920 according to the first embodiment. In the implantation candidate position display image 900c, a figure 60 indicating an implantation candidate position is superimposed on the brain function image 920b. Note that there are two activation regions 80a and 80b on the brain function image 920b in Figure 9. In the example shown in Figure 9, it is assumed that activation region 80a is selected by the user as the target of measurement. In this case, the display control function 157 displays the figure 60 at the position that is the shortest distance from activation region 80a among the vascular regions where the vessel diameter is greater than or equal to a specified size. Note that if the user selects activation region 80b, the display control function 157 displays the figure 60 at the position that is the shortest distance from activation region 80b among the vascular regions where the vessel diameter is greater than or equal to a specified size.

[0063] Furthermore, in Figure 9, the display control function 157 displays the text "Distance: 4mm" in the display field 61b on the implantation candidate position display image 900c, indicating the distance from the target position to the implantation candidate position. This display allows the user to understand that if an intravascular electrode is implanted at the implantation candidate position indicated by the figure 60, the distance between the target position (activation area) and the intravascular electrode will be 4mm.

[0064] Figure 10 shows an example of an implantation candidate position display image 900d using the nerve fiber image 930 according to the first embodiment. In the implantation candidate position display image 900d, a figure 60 indicating the implantation candidate position is superimposed on the nerve fiber image 930.

[0065] Furthermore, in Figure 10, the display control function 157 displays the text "Distance: 6mm" in the display field 61c on the implantation candidate position display image 900d, indicating the distance from the target position to the implantation candidate position.

[0066] The image used in the implantation candidate position display image 900, and the display mode of the implantation candidate position display image 900, may be predetermined, or they may be selectable or changeable by the user. For example, the display control function 157 may display a selection screen in which the user can select which of the following to use as the background image for the implantation candidate position display image 900: a cerebral blood vessel image 910, a brain function image 920, a nerve fiber image 930, a blood vessel diameter image 911, a brain function distance image 921, and a nerve fiber endpoint distance image 931. In addition, as shown in Figures 8 to 10, the user may select or change whether to display text information indicating the blood vessel diameter at the implantation candidate position, or the distance from the target position to the implantation candidate position, along with the implantation candidate position display image 900.

[0067] Furthermore, the display control function 157 may include a list box or the like outside the display area of ​​the candidate placement position display image 900 on the display 140, which allows switching between the type of background image of the candidate placement position display image 900 and the type of text information displayed.

[0068] Furthermore, the display control function 157 displays, for example, a confirmation button on the display 140 along with the candidate placement position display image 900, which can accept user input to determine the placement position. When the user presses this confirmation button, the candidate placement position indicated by the figure 60 on the candidate placement position display image 900 is determined as the placement position for the intravascular electrode.

[0069] Here, we will explain the process for identifying candidate positions for intravascular electrode placement, which is performed by the medical image processing device 100a configured as described above.

[0070] Figure 11 is a flowchart showing an example of the process for identifying candidate implantation locations for intravascular electrodes according to the first embodiment.

[0071] First, the acquisition function 152 acquires cerebral vascular images 910, brain function images 920, and nerve fiber images 930 of the subject (S1).

[0072] Then, the vascular diameter image generation function 153 generates a vascular diameter image 911 from the cerebral vascular image 910 (S2).

[0073] Furthermore, the brain distance image generation function 154 generates a brain functional distance image 921 from the brain functional image 920 (S3).

[0074] Furthermore, the nerve fiber endpoint distance image generation function 155 generates a nerve fiber endpoint distance image 931 from the nerve fiber image 930 (S4).

[0075] Then, the identification function 156 identifies candidate implantation locations for intravascular electrodes in the vascular region of the subject's brain based on the vascular diameter image 911, the brain function distance image 921, and the nerve fiber endpoint distance image 931 (S5). For example, the identification function 156 aligns the vascular diameter image 911, the brain function distance image 921, and the nerve fiber endpoint distance image 931 on a pixel-by-pixel basis, and identifies the pixel with the smallest distance from the target location among the pixels where the vascular diameter is greater than or equal to a specified size as a candidate implantation location. If there are two or more activation regions 80 in the subject's brain at the same time, the user may select which of the multiple activation regions 80 to measure before processing in S5.

[0076] The display control function 157 then displays an implantation candidate position display image 900 on the display 140, which is an image of the subject's brain (for example, a cerebral vascular image 910, a brain function image 920, a nerve fiber image 930, a vascular diameter image 911, a brain function distance image 921, or a nerve fiber endpoint distance image 931) on which a figure 60 indicating the implantation candidate position identified by the identification function 156 is superimposed (S6). The display control function 157 also displays a confirmation button on the display 140 along with the implantation candidate position display image 900, which can accept user input to determine the implantation position.

[0077] The reception function 151 accepts an operation by the user to determine the implantation location after they have confirmed the implantation candidate location display image 900 displayed on the display 140 (S7). For example, the reception function 151 may accept an operation by the user to press the confirm button displayed on the display 140. When the confirm button is pressed by the user, the implantation location candidates displayed on the implantation candidate location display image 900 are stored as implantation locations in the memory circuit 120. The determined implantation location may also be transmitted to another device via the NW interface 110. The other device may be, for example, a modality such as an angiography system used in interventional therapy for implantation of intravascular electrodes.

[0078] Furthermore, the reception function 151 may accept user operations to change the candidate placement location on the candidate placement location display image 900. For example, if the user changes the position of figure 60 on the candidate placement location display image 900, the reception function 151 may accept the changed position of figure 60 as the placement location. The processing of this flowchart ends when the placement location is determined in the S7 process.

[0079] As described above, the medical image processing device 100a of this embodiment generates a vessel diameter image 911 representing the size of the vessel diameter inside the brain from the cerebral vessel image 910, and identifies a target location corresponding to the neural activity to be measured inside the brain based on the brain function image 920 and the nerve fiber image 930, and generates a brain function distance image 921 and a nerve fiber endpoint distance image 931 showing the distance to the identified target location. Based on the generated vessel diameter image 911, brain function distance image 921, and nerve fiber endpoint distance image 931, the medical image processing device 100a of this embodiment identifies a location on the vessel where both the vessel diameter and the distance from the target location satisfy a predetermined criterion as a candidate for implantation of an intravascular electrode, and highlights the candidate implantation location on an image representing the brain (for example, the cerebral vessel image 910, brain function image 920, nerve fiber image 930, vessel diameter image 911, brain function distance image 921, or nerve fiber endpoint distance image 931). Therefore, according to the medical image processing device 100a of this embodiment, the user can easily identify candidate placement locations where the intravascular electrode can be placed near the site of nerve activity in the brain that is the target of measurement. As a result, the user can easily place the intravascular electrode in an ideal location for measuring nerve activity, thereby suppressing a decrease in measurement sensitivity.

[0080] Furthermore, the medical image processing device 100a of this embodiment generates a brain function distance image 921 representing the distance from the activated region of neural activity in the brain, and a nerve fiber endpoint distance image 931 representing the distance from the endpoint of the nerve fiber to be measured in the brain, as distance images for identifying the target location corresponding to the neural activity to be measured inside the brain. In this embodiment, the cerebral vascular image 910 used to generate the vascular diameter image 911, the brain function image 920 used to generate the brain function distance image 921, and the nerve fiber image 930 used for the nerve fiber endpoint distance image 931 are all images taken of the brain of the same subject. Therefore, according to the medical image processing device 100a of this embodiment, the target location can be accurately identified by two types of distance images generated from images taken of the brain of the same subject. In addition, because all images used to identify the implantation location candidate are images taken of the brain of the same subject, the accuracy of alignment between images for identifying the vascular diameter and the distance from the target location is increased, and suitable implantation location candidates can be accurately identified.

[0081] Furthermore, the blood vessel diameter image 911 in this embodiment is an image that represents the size of the blood vessel diameter inside the brain in terms of pixel values, and the brain function distance image 921 and the nerve fiber endpoint distance image 931 are images that represent the distance from the target position in terms of pixel values. Based on the pixel values ​​of the blood vessel diameter image 911, the pixel values ​​of the brain function distance image 921, and the pixel values ​​of the nerve fiber endpoint distance image 931, the medical image processing device 100a in this embodiment identifies the pixel with the smallest distance from the target position among the pixels where the blood vessel diameter is greater than or equal to a specified size as a candidate implantation position. Therefore, according to the medical image processing device 100a in this embodiment, candidate implantation positions for intravascular electrodes suitable for measuring the target nerve activity can be identified with high precision on a pixel-by-pixel basis.

[0082] Furthermore, the medical image processing device 100a of this embodiment displays characters representing the blood vessel diameter at the implantation site candidate, or the distance to the target location, on an image representing the brain. Therefore, with the medical image processing device 100a of this embodiment, the user can easily grasp the blood vessel diameter at the highlighted implantation site candidate, or the distance between the target location and the intravascular electrode when an intravascular electrode is implanted. This allows the user to visually confirm the implantation site candidate while simultaneously considering the blood vessel diameter at the implantation site candidate, or the distance to the target location, as indicated by the character information, when deciding on an implantation site.

[0083] (Second embodiment) Figure 12 is a diagram illustrating the overview of the input and output of the processing of a medical image processing device according to the second embodiment. In the first embodiment described above, the medical image processing device 100a took a cerebral vascular image 910, a brain function image 920, and a nerve fiber image 930 as inputs and output an implantation candidate position display image 900. However, the medical image processing device does not necessarily have to use both the brain function image 920 and the nerve fiber image 930, and may identify a target position corresponding to the neural activity to be measured inside the brain based on an image relating to at least one of the brain function image 920 and the nerve fiber image 930. Specifically, in this second embodiment, the medical image processing device takes a cerebral vascular image 910 and a brain function image 920 as inputs and outputs an implantation candidate position display image 900.

[0084] Similar to the first embodiment, the cerebral angiography image 910 is an image in which the blood vessels of the subject's brain are visualized. Also, similar to the first embodiment, the brain function image 920 is an image in which the activated region of neural activity to be measured by intravascular electrodes such as fMRI images is visualized.

[0085] Figure 13 shows an example of the configuration of a medical image processing apparatus 100b according to the second embodiment. Similar to the medical image processing apparatus 100a of the first embodiment shown in Figure 2, the medical image processing apparatus 100b includes an NW interface 110, a storage circuit 120, an input interface 130, a display 140, and a processing circuit 150.

[0086] Furthermore, the processing circuit 150 includes a reception function 151, an acquisition function 152a, a blood vessel diameter image generation function 153, a brain distance image generation function 154, a specific function 156a, and a display control function 157.

[0087] The reception function 151, the blood vessel diameter image generation function 153, the brain distance image generation function 154, and the display control function 157 have the same functions as in the first embodiment.

[0088] For example, the blood vessel diameter image generation function 153 generates a blood vessel diameter image 911 from the cerebral blood vessel image 910, similar to the first embodiment.

[0089] Furthermore, the brain distance image generation function 154 generates a brain functional distance image 921 from the brain functional image 920, similar to the first embodiment. The brain functional distance image 921 is an example of a distance image in this embodiment.

[0090] Furthermore, the display control function 157, similar to the first embodiment, displays an implantation candidate position display image 900 on the display 140, in which the implantation candidate positions are highlighted on an image representing the subject's brain. In this embodiment, the image representing the subject's brain used in the implantation candidate position display image 900 is, for example, a cerebral vascular image 910, a brain function image 920, a vascular diameter image 911, or a brain function distance image 921.

[0091] The acquisition function 152a acquires cerebral vascular images 910 and brain functional images 920, for example, via the NW interface 110.

[0092] The cerebral vascular image 910 and brain function image 920 acquired by the acquisition function 152a are images taken of the brain of the same subject.

[0093] Unlike the specific function 156 of the first embodiment, the specific function 156a identifies candidate implantation locations without using nerve fiber endpoint distance images 931. In other respects, the specific function 156a has the same functions as the specific function 156 of the first embodiment.

[0094] More specifically, the identification function 156a identifies candidate placement locations for intravascular electrodes based on the vascular diameter image 911 and the brain function distance image 921. In this embodiment, the identification function 156a aligns the vascular diameter image 911 and the brain function distance image 921 on a pixel-by-pixel basis and identifies the distance between the vascular diameter and the target location at each pixel based on the pixel values ​​of the vascular diameter image 911 and the pixel values ​​of the brain function distance image 921. Then, the identification function 156a identifies a pixel as a candidate placement location if, among the pixels where the vascular diameter is greater than or equal to a specified size, the pixel with the smallest distance from the target location is less than or equal to the specified distance from the target location.

[0095] Here, we will describe the process for identifying candidate implantation locations for intravascular electrodes, which is performed by the medical image processing device 100b of this embodiment configured as described above. Figure 14 is a flowchart showing an example of the process for identifying candidate implantation locations for intravascular electrodes according to the second embodiment.

[0096] First, the acquisition function 152a acquires cerebral vascular images 910 and brain function images 920 of the subject (S11).

[0097] Then, the vascular diameter image generation function 153 generates a vascular diameter image 911 from the cerebral vascular image 910 (S12).

[0098] Furthermore, the brain distance image generation function 154 generates a brain functional distance image 921 from the brain functional image 920 (S13).

[0099] Then, the specific function 156a identifies candidate placement locations for intravascular electrodes in the vascular region of the subject's brain based on the vascular diameter image 911 and the brain function distance image 921 (S14).

[0100] The display control function 157 then displays an implantation candidate position display image 900 on the display 140, which is an image of the subject's brain (for example, a cerebral vascular image 910, a brain function image 920, a vascular diameter image 911, or a brain function distance image 921) on which a figure 60 indicating the implantation candidate position identified by the identification function 156a is superimposed (S15).

[0101] The reception function 151 then accepts an operation from the user to determine the implantation position after they have confirmed the implantation candidate position display image 900 shown on the display 140 (S16). The processing after determining the implantation position is the same as in the first embodiment. This concludes the processing in this flowchart.

[0102] Thus, the medical image processing device 100b of this embodiment generates a brain function distance image 921 representing the distance from the area of ​​activated neural activity in the brain from the brain function image 920, and identifies candidate implantation locations for intravascular electrodes based on the cerebral vascular image 910 and the brain function distance image 921. Therefore, according to the medical image processing device 100b of this embodiment, in addition to the same effects as the first embodiment, candidate implantation locations for intravascular electrodes can be identified even when it is difficult to obtain nerve fiber images 930.

[0103] Furthermore, in this embodiment, the cerebral vascular image 910 used to generate the vascular diameter image 911 and the brain function image 920 used to generate the brain function distance image 921 are images taken of the brain of the same subject. Therefore, according to the medical image processing device 100b of this embodiment, similar to the first embodiment, by accurately performing alignment between images for the purpose of determining the vascular diameter and the distance from the target position, it is possible to accurately identify candidate implantation positions suitable for measurement.

[0104] (Third embodiment) Figure 15 is a diagram illustrating the overview of the input and output of the processing of the medical image processing apparatus according to the third embodiment. In the first embodiment described above, the medical image processing apparatus 100a took a cerebral vascular image 910, a brain function image 920, and a nerve fiber image 930 as inputs and output an implantation candidate position display image 900. In contrast, in this third embodiment, the medical image processing apparatus takes a cerebral vascular image 910 and a nerve fiber image 930 as inputs and outputs an implantation candidate position display image 900.

[0105] Similar to the first embodiment, the cerebral angiography image 910 is an image in which the blood vessels of the subject's brain are visualized. Also, similar to the first embodiment, the nerve fiber image 930 is an image in which nerve fibers in the brain are visualized, such as an MRI tractography.

[0106] Figure 16 shows an example of the configuration of a medical image processing apparatus 100c according to the third embodiment. Similar to the medical image processing apparatus 100a of the first embodiment shown in Figure 2, the medical image processing apparatus 100c includes an NW interface 110, a storage circuit 120, an input interface 130, a display 140, and a processing circuit 150.

[0107] Furthermore, the processing circuit 150 includes a reception function 151, an acquisition function 152b, a blood vessel diameter image generation function 153, a nerve fiber endpoint distance image generation function 155, a identification function 156b, and a display control function 157.

[0108] The reception function 151, the blood vessel diameter image generation function 153, the nerve fiber endpoint distance image generation function 155, and the display control function 157 have the same functions as in the first embodiment.

[0109] For example, the blood vessel diameter image generation function 153 generates a blood vessel diameter image 911 from the cerebral blood vessel image 910, similar to the first embodiment.

[0110] Furthermore, the nerve fiber endpoint distance image generation function 155 generates a nerve fiber endpoint distance image 931 from the nerve fiber image 930, similar to the first embodiment. The nerve fiber endpoint distance image 931 is an example of a distance image in this embodiment.

[0111] Furthermore, the display control function 157, similar to the first embodiment, displays a candidate implantation position display image 900 on the display 140, in which the candidate implantation positions are highlighted on an image representing the subject's brain. In this embodiment, the image representing the subject's brain used in the candidate implantation position display image 900 is, for example, a cerebral blood vessel image 910, a nerve fiber image 930, a blood vessel diameter image 911, or a nerve fiber endpoint distance image 931.

[0112] The acquisition function 152b acquires cerebral vascular images 910 and nerve fiber images 930, for example, via the NW interface 110.

[0113] The cerebral vascular images 910 and nerve fiber images 930 acquired by the acquisition function 152b are images taken of the brain of the same subject.

[0114] Unlike the specific function 156 of the first embodiment, the specific function 156b identifies candidate implantation locations without using brain functional distance images 921. In other respects, the specific function 156b has the same functions as the specific function 156 of the first embodiment.

[0115] More specifically, the identification function 156b identifies candidate placement locations for intravascular electrodes based on the vessel diameter image 911 and the nerve fiber endpoint distance image 931. In this embodiment, the identification function 156b aligns the vessel diameter image 911 and the nerve fiber endpoint distance image 931 on a pixel-by-pixel basis, and identifies the distance between the vessel diameter and the target location at each pixel based on the pixel values ​​of the vessel diameter image 911 and the pixel values ​​of the nerve fiber endpoint distance image 931. Then, the identification function 156b identifies a pixel as a candidate placement location if, among the pixels where the vessel diameter is greater than or equal to a specified size, the pixel with the smallest distance from the target location is less than or equal to the specified distance from the target location.

[0116] Here, we will describe the process for identifying candidate implantation locations for intravascular electrodes, which is performed by the medical image processing device 100c of this embodiment configured as described above. Figure 17 is a flowchart showing an example of the process for identifying candidate implantation locations for intravascular electrodes according to the third embodiment.

[0117] First, the acquisition function 152b acquires cerebral vascular images 910 and nerve fiber images 930 of the subject (S21).

[0118] Then, the blood vessel diameter image generation function 153 generates a blood vessel diameter image 911 from the cerebral blood vessel image 910 (S22).

[0119] Furthermore, the nerve fiber endpoint distance image generation function 155 generates a nerve fiber endpoint distance image 931 from the nerve fiber image 930 (S23).

[0120] Then, the specific function 156b identifies candidate placement locations for intravascular electrodes in the vascular region of the subject's brain based on the vascular diameter image 911 and the nerve fiber endpoint distance image 931 (S24).

[0121] Then, the display control function 157 causes the display 140 to display an implantation candidate position display image 900, which is an image of the subject's brain (for example, a cerebral blood vessel image 910, a nerve fiber image 930, a blood vessel diameter image 911, or a nerve fiber endpoint distance image 931) on which a figure 60 indicating the implantation candidate position identified by the identification function 156b is superimposed (S25).

[0122] The reception function 151 then accepts an operation from the user to determine the implantation position after they have confirmed the implantation candidate position display image 900 shown on the display 140 (S26). The processing after determining the implantation position is the same as in the first embodiment. This concludes the processing in this flowchart.

[0123] Thus, the medical image processing device 100c of this embodiment generates a nerve fiber endpoint distance image 931 representing the distance from the endpoint of the nerve fiber to be measured from the nerve fiber image 930, and identifies candidate implantation positions for intravascular electrodes based on the cerebral blood vessel image 910 and the nerve fiber endpoint distance image 931. For this reason, in addition to the same effects as the first embodiment, the medical image processing device 100c of this embodiment can identify candidate implantation positions for intravascular electrodes even when it is difficult to obtain a brain function image 920.

[0124] Furthermore, in this embodiment, the cerebral vascular image 910 used to generate the vascular diameter image 911 and the nerve fiber image 930 used to generate the nerve fiber endpoint distance image 931 are images taken of the brain of the same subject. Therefore, according to the medical image processing device 100c of this embodiment, similar to the first embodiment, by accurately performing alignment between images for determining the vascular diameter and the distance from the target position, suitable implantation position candidates for measurement can be accurately identified.

[0125] (Fourth embodiment) Figure 18 is a diagram illustrating the overview of the input and output of the processing of the medical image processing apparatus according to the fourth embodiment. In the first embodiment described above, the medical image processing apparatus 100a took a cerebral vascular image 910, a brain function image 920, and a nerve fiber image 930 as inputs and output an implantation candidate position display image 900. In contrast, in this fourth embodiment, the medical image processing apparatus takes a cerebral vascular image 910 and a brain region atlas 940 as inputs and outputs an implantation candidate position display image 900.

[0126] Similar to the first embodiment, the cerebral vascular image 910 is an image in which the blood vessels of the subject's brain are visualized.

[0127] Brain Region Atlas 940 is information showing the anatomical arrangement of brain regions responsible for neural activity. More specifically, Brain Region Atlas 940 associates brain functions with the three-dimensional coordinates of the regions that control those functions. Brain Region Atlas 940 is also called a brain atlas, brain map, or brain function mapping. Brain Region Atlas 940 is not subject-specific information, but information common to the general human brain. Brain Region Atlas 940 is an example of an image relating to the functional regions of the brain in this embodiment.

[0128] Figure 19 shows an example of the configuration of a medical image processing apparatus 100d according to the fourth embodiment. Similar to the medical image processing apparatus 100a of the first embodiment shown in Figure 2, the medical image processing apparatus 100d includes an NW interface 110, a storage circuit 120, an input interface 130, a display 140, and a processing circuit 150.

[0129] Furthermore, the processing circuit 150 includes a reception function 151, an acquisition function 152c, a blood vessel diameter image generation function 153, a identification function 156c, a display control function 157, and a brain region distance image generation function 158. The brain region distance image generation function 158 is an example of a distance image generation unit in this embodiment.

[0130] The reception function 151, the blood vessel diameter image generation function 153, and the display control function 157 have the same functions as in the first embodiment.

[0131] For example, the blood vessel diameter image generation function 153 generates a blood vessel diameter image 911 from the cerebral blood vessel image 910, similar to the first embodiment.

[0132] Furthermore, the display control function 157, similar to the first embodiment, displays a candidate implantation position display image 900 on the display 140, in which the candidate implantation positions are highlighted on an image representing the subject's brain. In this embodiment, the image representing the subject's brain used in the candidate implantation position display image 900 is, for example, a cerebral vascular image 910 or a vascular diameter image 911.

[0133] The acquisition function 152c acquires cerebral vascular images 910 and brain region atlases 940, for example, via the NW interface 110. The source of the brain region atlases 940 is not particularly limited, but may be information from the internet, for example. The brain region atlases 940 may also be stored in the memory circuit 120 beforehand. In this case, the acquisition function 152c reading the brain region atlases 940 from the memory circuit 120 is also considered an example of acquisition.

[0134] The brain region distance image generation function 158 generates brain region distance images from the brain region atlas 940. The brain region distance image is an example of a distance image in this embodiment.

[0135] Figure 20 shows an example of a brain region atlas 940 and a brain region distance image 941 according to the fourth embodiment.

[0136] The brain region distance image 941 is an image that represents the distance from the brain region where neural activity is being measured. The brain region distance image generation function 158 generates the brain region distance image 941 by, for example, setting the distance from the brain region where neural activity is being measured to each pixel as the pixel value for all pixels. In other words, the brain region distance image 941 is an image that represents the distance from the brain region where neural activity is being measured to the position of each pixel as a pixel value (grayscale).

[0137] The brain region targeted for neural activity measurement is an example of the target location in this embodiment. The brain region targeted for neural activity measurement is selected, for example, by the user. The display control function 157 may display a selection screen on the display 140 that accepts user input to specify the brain region to be measured on the brain region atlas 940. The reception function 151 may also accept user input to specify the brain region to be measured.

[0138] Note that in Figure 20, a dashed line is shown to indicate the range of the brain region at the same distance from the brain region where neural activity is measured in the brain region distance image 941. This is a representation that emphasizes the boundaries of pixel values ​​(shades) for illustrative purposes, and the dashed line does not actually need to be displayed.

[0139] Unlike the specific function 156 of the first embodiment, the specific function 156c identifies candidate implantation positions for intravascular electrodes based on the vascular diameter image 911 and the brain region distance image 941. The specific function 156c of this embodiment aligns the vascular diameter image 911 and the brain region distance image 941 on a pixel-by-pixel basis and identifies the distance between the vascular diameter and the target position at each pixel based on the pixel values ​​of the vascular diameter image 911 and the pixel values ​​of the brain region distance image 941. Then, the specific function 156c identifies a pixel as an implantation position candidate if, among the pixels where the vascular diameter is greater than or equal to a specified size, the pixel with the smallest distance from the target position is less than or equal to the specified distance from the target position. Note that the brain region atlas 940 is not corresponding to an individual subject but is a model assuming a general human body, so the specific function 156c may correct the brain region distance image 941 to match the vascular diameter image 911 when aligning the vascular diameter image 911 and the brain region distance image 941.

[0140] Here, we will describe the process for identifying candidate implantation locations for intravascular electrodes, which is performed by the medical image processing device 100d of this embodiment configured as described above. Figure 21 is a flowchart showing an example of the process for identifying candidate implantation locations for intravascular electrodes according to the fourth embodiment.

[0141] First, the acquisition function 152c acquires cerebral angiography images 910 and brain region atlases 940 (S31).

[0142] Then, the blood vessel diameter image generation function 153 generates a blood vessel diameter image 911 from the cerebral blood vessel image 910 (S32).

[0143] Furthermore, the brain region distance image generation function 158 generates brain region distance images 941 from the brain region atlas 940 (S33).

[0144] Then, the specific function 156c identifies candidate placement locations for intravascular electrodes in the vascular region of the subject's brain based on the vascular diameter image 911 and the brain region distance image 941 (S34).

[0145] Then, the display control function 157 causes the display 140 to display an implantation candidate position display image 900, which is an image of the subject's brain (for example, a cerebral blood vessel image 910 or a blood vessel diameter image 911) on which a figure 60 indicating the implantation candidate position identified by the identification function 156c is superimposed (S35).

[0146] The reception function 151 then accepts an operation from the user to determine the implantation position after they have confirmed the implantation position candidate image 900 displayed on the display 140 (S36). The processing after determining the implantation position is the same as in the first embodiment. This concludes the processing in this flowchart.

[0147] Thus, the medical image processing device 100d of this embodiment identifies candidate intravascular electrode placement positions based on a vascular diameter image 911 generated from a cerebral vascular image 910 and a brain region distance image 941 generated from a brain region atlas 940. Therefore, according to the medical image processing device 100b of this embodiment, in addition to the same effects as the first embodiment, candidate intravascular electrode placement positions can be identified even when it is difficult to obtain brain function images 920 and nerve fiber images 930.

[0148] For example, MRI imaging may be difficult if a magnetic device is implanted in the subject's body, the subject is using an artificial joint, or a magnetic foreign object is present in the subject's brain due to a past accident. Since the medical image processing device 100d of this embodiment does not use brain function images 920 and nerve fiber images 930, if, for example, CT angiography or DSA is used as the cerebral vascular image 910, it is possible to identify candidate implantation sites for intravascular electrodes without performing MRI imaging. For this reason, this fourth embodiment can be applied even in situations where the first to third embodiments are difficult to apply.

[0149] (Variation 1) In the first to fourth embodiments described above, the specific functions 156, 156a to 156c identify a pixel as a candidate implantation location if, among pixels whose blood vessel diameter is greater than or equal to a specified size, the pixel with the minimum distance from the target location is less than or equal to a specified distance from the target location. However, it is not always the case that there is a pixel among the pixels whose blood vessel diameter is greater than or equal to a specified size that is less than or equal to a specified distance from the target location.

[0150] For example, if there is no location on the blood vessel where both the blood vessel diameter and the distance from the target location meet the specified criteria, the specific functions 156, 156a to 156c may identify a location that satisfies either the blood vessel diameter or the distance from the target location as an alternative candidate. For example, if there is no pixel with a blood vessel diameter equal to or greater than the specified size within the range where the distance from the target location is less than or equal to the specified distance, the specific function 156 may identify a pixel with a blood vessel diameter equal to or greater than the specified size among the pixels adjacent to the pixel with the minimum distance from the target location as a candidate implantation location.

[0151] If there are no pixels with a blood vessel diameter greater than or equal to a specified size that are less than or equal to a specified distance from the target position, the specific functions 156, 156a to 156c may identify the corresponding pixels when either or both of the blood vessel diameter and / or distance from the target position in the specified standard are changed. For example, the specific functions 156, 156a to 156c may identify "how many millimeters the blood vessel diameter needs to be increased from the current specified standard to find a pixel that is less than or equal to a specified distance from the target position," or "how many millimeters the upper limit of the distance from the target position needs to be increased from the current specified distance to find a pixel whose blood vessel diameter meets the current specified standard."

[0152] Furthermore, if there are no pixels within a specified distance from the target position that are within a specified distance of the target position, the identification functions 156, 156a to 156c may output a result such as "cannot identify a candidate implantation position." In this case, the display control function 157 may display on the display 140 that "there are no (or cannot identify) blood vessels that meet the specified criteria" instead of the implantation candidate position display image 900.

[0153] Furthermore, the display control function 157 may display on the display 140 the identified alternative candidate and suggestions regarding changes to the prescribed criteria based on the vessel diameter and distance from the target position at the location of the alternative candidate. For example, the display control function 157 may display on the display 140 text information indicating a suggestion such as, "Among the locations that satisfy the currently specified vessel diameter (or catheter diameter), the closest distance from the target position is X mm," or "Among the vessels that satisfy the distance from the currently specified target position, the diameter of the thickest vessel is Y mm."

[0154] Furthermore, the reception function 151 may accept a user operation to change the prescribed criteria if there is no position on the blood vessel where both the blood vessel diameter and the distance from the target position meet the prescribed criteria. In this case, the identification functions 156, 156a to 156c identify the implantation position candidates again based on the changed prescribed criteria. The display control function 157 also displays an implantation position candidate display image 900 on the display 140, highlighting the implantation position candidates identified based on the changed prescribed criteria.

[0155] By displaying alternative candidates and suggestions regarding changes to the defined criteria, users can appropriately modify the defined criteria by referring to the displayed alternative candidates and suggestions. For example, if a location that meets the defined criteria is simply not identified, users will not know how to modify the defined criteria to find the corresponding pixels, which may lead to wasted effort in considering the changes or the criteria being relaxed more than necessary. Displaying alternative candidates and suggestions regarding changes to the defined criteria can support the user's work and contribute to the efficiency of identifying potential placement locations.

[0156] (Modification 2) Furthermore, in the first embodiment described above, the identification function 156 may identify multiple implantation location candidates if there are two or more locations with the minimum distance from the target location. When there are multiple implantation location candidates that meet the specified criteria, the display control function 157 may number the multiple implantation location candidates in order of decreasing vessel diameter or in order of proximity to the target location and display them on the implantation candidate location display image 900.

[0157] By prioritizing and displaying multiple potential implantation locations according to vessel diameter or distance from the target location, it is possible to support users in making appropriate decisions when determining the implantation location.

[0158] (Variation 3) In the first to fourth embodiments described above, intravascular electrodes were used as an example of a device. However, the devices implanted in the subject are not limited to intravascular electrodes, and the medical image processing devices 100a to 100 of the first to fourth embodiments are applicable to determining the implantation position of various devices.

[0159] The various types of data discussed in this specification are typically digital data.

[0160] According to at least one embodiment described above, it is possible for the user to easily identify candidate placement locations where intravascular electrodes can be placed near the source of the target neural activity in the brain.

[0161] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be implemented in a variety of other forms, and various omissions, substitutions, modifications, and combinations of embodiments are possible without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0162] 60 shapes 61a~61c Display field 70a, 70b nerve fibers 71a, 71b Nerve fiber endpoints 80, 80a, 80b Activation areas 100, 100a, 100b, 100c, 100d Medical Image Processing Device 110 NW Interfaces 120 Memory circuit 130 Input Interfaces 140 displays 150 Processing Circuits 151 Reception function 152,152a,152b,152c acquisition function 153. Blood vessel diameter image generation function 154 Brain Distance Image Generation Function 155 Nerve fiber endpoint distance image generation function 156, 156a, 156b, 156c Specific Functions 157 Display control function 158 Brain Region Distance Image Generation Function Images showing candidate placement locations for 900, 900a, 900b, 900c, and 900d. 910 Cerebral blood vessel imaging 911 Vascular diameter image 920, 920a, 920b Brain function images 921, 921a, 921b Brain function distance images 930 Nerve fiber images 931 Nerve fiber endpoint distance image 940 Brain Region Atlas 941 Brain Region Distance Imaging

Claims

1. A vascular diameter image generation unit generates a vascular diameter image representing the size of the blood vessels inside the brain from a cerebral vascular image obtained by photographing the blood vessels of the brain, A distance image generation unit identifies a target location corresponding to the neural activity to be measured inside the brain, based on an image relating to at least one of the functional regions of the brain and the arrangement of nerves inside the brain, and generates a distance image indicating the distance to the identified target location. An implantation position candidate identification unit identifies a position on the blood vessel where both the blood vessel diameter and the distance from the target position satisfy a specified criterion, based on the blood vessel diameter image and the distance image, as a candidate implantation position for the device. A display control unit that highlights the identified candidate implantation locations on an image representing the brain, A medical image processing device equipped with [a specific feature].

2. The distance image includes a brain function distance image representing the distance from the area of ​​neural activity activation in the brain, and a nerve fiber endpoint distance image representing the distance from the endpoint of the nerve fiber to be measured in the brain. The distance image generation unit generates a brain function distance image from a brain function image in which the activated region is visualized, and generates a nerve fiber endpoint distance image from a nerve fiber image in which the nerve fibers in the brain are visualized. The implantation location candidate identification unit identifies the implantation location candidate based on the blood vessel diameter image, the brain function distance image, and the nerve fiber endpoint distance image. The cerebral vascular images, brain function images, and nerve fiber images are images taken of the brain of the same subject. The medical image processing apparatus according to claim 1.

3. The aforementioned distance image is a brain function distance image that represents the distance from the area of ​​neural activity activation in the brain. The distance image generation unit generates the brain function distance image from the brain function image in which the activated region is visualized. The implantation location candidate identification unit identifies the implantation location candidate based on the vascular diameter image and the brain function distance image. The cerebral vascular images and brain function images are images taken of the brain of the same subject. The medical image processing apparatus according to claim 1.

4. The distance image is a nerve fiber endpoint distance image representing the distance from the endpoint of the nerve fiber to be measured in the brain. The distance image generation unit generates nerve fiber endpoint distance images from nerve fiber images in which the nerve fibers in the brain are visualized. The implantation location candidate identification unit identifies the implantation location candidate based on the blood vessel diameter image and the nerve fiber endpoint distance image. The cerebral blood vessel images and the nerve fiber images are images taken of the brain of the same subject. The medical image processing apparatus according to claim 1.

5. The aforementioned distance image is a brain region distance image that represents the distance from the region in the brain where neural activity is to be measured. The distance image generation unit generates the brain region distance image from a brain region atlas showing the anatomical arrangement of the brain regions responsible for the neural activity. The implantation location candidate identification unit identifies the implantation location candidate based on the vascular diameter image and the brain region distance image. The medical image processing apparatus according to claim 1.

6. The aforementioned blood vessel diameter image is an image that represents the size of the blood vessels inside the brain in terms of pixel values. The aforementioned distance image is an image in which the distance from the target position is represented by pixel values. The implantation position candidate identification unit identifies, based on the pixel values ​​of the blood vessel diameter image and the pixel values ​​of the distance image, the pixel whose distance from the target position is the smallest among the pixels whose blood vessel diameter is equal to or greater than a specified size, as the implantation position candidate. The medical image processing apparatus according to claim 1.

7. The display control unit further displays characters representing the blood vessel diameter at the candidate implantation location, or the distance to the target location, on the image representing the brain. The medical image processing apparatus according to claim 1.

8. If there are multiple candidate implantation locations that meet the specified criteria, the display control unit will number and display the multiple candidate implantation locations in order of decreasing vessel diameter or in order of proximity to the target location. The medical image processing apparatus according to claim 1.

9. If there is no location on the blood vessel where both the blood vessel diameter and the distance from the target location satisfy the specified criteria, the implantation location candidate identification unit identifies a location that satisfies either the blood vessel diameter or the distance from the target location as an alternative candidate. The display control unit displays the alternative candidate and a suggestion regarding the modification of the prescribed criteria based on the blood vessel diameter at the location of the alternative candidate and the distance from the target location. The medical image processing apparatus according to claim 1.

10. The system further includes a reception unit that accepts user input to modify the specified criteria when there is no position on the blood vessel where both the blood vessel diameter and the distance from the target position satisfy the specified criteria. A medical image processing apparatus according to any one of claims 1 to 9.

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