Medical image processing apparatus, medical image processing method, and medical image processing program

Abstract

To improve analysis accuracy of a region and properties of a target site of a subject in medical diagnosis using MR images and CT images.SOLUTION: A medical image processing apparatus according to one embodiment includes a first acquisition unit, a second acquisition unit, an extraction unit, a setting unit, and a first analysis unit. The first acquisition unit acquires a MR image of a subject acquired by a MRI device. The second acquisition unit acquires a CT image of the subject acquired by an X-ray CT device. The extraction unit extracts a target site of the subject from a first image being one of the MR image and the CT image. The setting unit sets a seed point in the target site of the subject in the first image. The first analysis unit determines the target site of the subject with high precision in a second image being the other image different from the one of the MR image and the CT image with the set seed point as a starting point.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The embodiments disclosed in this specification and the drawings relate to a medical image processing apparatus, a medical image processing method, and a medical image processing program. [Background technology]

[0002] An X-ray CT (Computed Tomography) device is a medical imaging diagnostic device that irradiates an object with X-rays and generates a tomographic image of the object based on the X-rays detected by an X-ray detector. In an X-ray CT device, a pair of an X-ray tube and an X-ray detector rotates around the object at high speed, continuously photographing the object and collecting a large amount of projection data, and a tomographic image is generated by reconstructing this large amount of projection data.

[0003] A magnetic resonance imaging (MRI) device is an imaging device that excites the nuclear spins of a subject placed in a static magnetic field with a radio frequency (RF) signal at the Larmor frequency, and generates an MR image by reconstructing the magnetic resonance signals (MR signals) generated from the subject in response to the excitation.

[0004] In medical diagnosis, diagnostic images obtained from multiple medical diagnostic devices such as an X-ray CT device, an MRI device, and an ultrasound device may be used. One example is the evaluation of plaque properties. Plaque has different plaque properties depending on its composition. Therefore, for example, both diagnostic images obtained from an X-ray CT device, which is relatively good at depicting hard plaque, and diagnostic images obtained from an MRI device, which is relatively good at depicting soft plaque, may be used in medical diagnosis. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2019-98164 A Summary of the Invention [Problem to be solved by the invention]

[0006] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to improve the accuracy of analysis of the region and characteristics of a site of interest in a subject in medical diagnosis using MR images and CT images. However, the problems to be solved by the embodiments disclosed in this specification and the drawings 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] A medical image processing apparatus according to an embodiment includes a first acquisition unit, a second acquisition unit, an extraction unit, a setting unit, and a first analysis unit. The first acquisition unit acquires an MR image of a subject acquired by an MRI device. The second acquisition unit acquires a CT image of the subject acquired by an X-ray CT device. The extraction unit extracts a region of interest of the subject from a first image, which is either an MR image or a CT image. The setting unit sets a seed point in the region of interest of the subject in the first image. The first analysis unit determines the region of interest of the subject with high resolution in a second image, which is the other image different from either the MR image or the CT image, using the set seed point as a starting point. [Brief description of the drawings]

[0008] [Figure 1] 1 is a schematic diagram showing an example of the arrangement of a medical image processing apparatus according to a first embodiment. [Diagram 2] FIG. 2 is a schematic diagram of a cross section of a blood vessel for explaining a region including plaque. [Diagram 3] FIG. 3 is a diagram for explaining the difference in how the regions including plaque in FIG. 2(B) and FIG. 2(C) are depicted in MR images and CT images. [Figure 4] 5 is a flowchart showing an example of the operation of the medical image processing apparatus according to the first embodiment. [Diagram 5] FIG. 2 is a view for explaining medical image processing according to the first embodiment. [Figure 6]FIG. 11 is a schematic diagram showing an example of the arrangement of a medical image processing apparatus according to a second embodiment. [Figure 7] 10 is a flowchart showing an example of the operation of a medical image processing apparatus according to the second embodiment. [Figure 8] 13A to 13C are views for explaining medical image processing according to the second embodiment. [Figure 9] FIG. 13 is a schematic diagram showing an example of the arrangement of a medical image processing apparatus according to a third embodiment. [Figure 10] 13 is a flowchart showing an example of the operation of a medical image processing apparatus according to the third embodiment. [Figure 11] 13A to 13C are views for explaining medical image processing according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, embodiments of a medical image processing apparatus, a medical image processing method, and a medical image processing program will be described in detail with reference to the drawings.

[0010] (First embodiment) 1 is a schematic diagram showing an example of the configuration of a medical image processing device 100 according to the first embodiment. The medical image processing device 100 is configured by a general image processing device such as a personal computer or a workstation. As shown in FIG. 1, the medical image processing device 100 includes a processing circuit 110 and a storage circuit 120. The medical image processing device 100 may further include an input interface 130, a network interface 140, and a display 150.

[0011] The display 150 is configured by a general display device such as a liquid crystal display panel, an OLED (Organic Light Emitting Diode) display panel, a plasma display panel, or an organic EL (Electro Luminescence) panel.

[0012] The network interface 140 implements various information communication protocols according to the form of the network. The network interface 140 has a function of performing communication control according to various protocols and being able to connect to the network by wire or wirelessly. The network interface 140 enables communication between the medical image processing device 100, the X-ray CT device 200, and the MRI device 300 via the network. The network interface 140 can transfer various data, such as an X-ray CT image of a subject, and an imaging method and imaging conditions suitable for examination of the subject by the MRI device, via the network.

[0013] The input interface 130 includes an input device that can be operated by a user, and an input circuit that inputs a signal from the input device. The input device is realized by, for example, a trackball, a switch, a mouse, a keyboard, a touch pad that performs an input operation by touching the scanning surface, a touch screen in which a display screen and a touch pad are integrated, a non-contact input device using an optical sensor, a voice input device, etc. When the input device is operated by a user, the input circuit generates a signal according to the operation and outputs it to the processing circuit 110. Note that the input interface 130 may be a touch panel in which the input device is integrated with a display.

[0014] The memory circuitry 120 is configured with a processor-readable storage medium, such as a semiconductor memory element such as a random access memory (RAM) or a flash memory, a hard disk, or an optical disk. The memory circuitry 120 may be configured with a portable medium, such as a universal serial bus (USB) memory or a digital video disk (DVD). The portable medium allows the medical image processing device 100 to acquire various data, such as a CT image of a subject and an MR image of the subject, from an X-ray CT device and an MRI device. The memory circuitry 120 also stores various processing programs used in the processing circuitry 110, data required for executing the programs, and the like.

[0015] The processing circuitry 110 realizes a function of controlling the medical image processing device 100. The processing circuitry 110 is also a processor that realizes various functions by reading and executing various processing programs stored in the storage circuitry 120. The processor of the processing circuitry 110 realizes each function such as a first acquisition function F1, a second acquisition function F2, an extraction function F3, a position correction function F4, a seed point setting function F5, a first analysis function F6, and a display function F7.

[0016] Here, as an example of medical diagnosis using multiple medical diagnostic devices, plaque analysis will be described with reference to Figures 2 and 3, but the site of interest of the subject to be analyzed is not limited to a plaque region. The site of interest of the subject may be any site that can be used in medical diagnosis, and may be a stenosis site.

[0017] FIG. 2 is a schematic diagram of a blood vessel cross section for explaining a region including plaque. Plaque generally refers to a patchy hypertrophic lesion of the intima present in an arteriosclerotic lesion. Plaque may cause stenosis of the blood vessel lumen. Plaque is also classified into hard plaque, soft plaque, stable plaque, unstable plaque, etc., depending on its composition. For example, unstable plaque is composed of lipid components, hemorrhagic components, fibrous components (e.g., fibrous cap), etc. Therefore, it is desired to have excellent contrast discrimination between each component, to determine a region of interest with high resolution, and to accurately analyze the properties of the region of interest.

[0018] Fig. 2(A) shows a case where no plaque has occurred, Fig. 2(B) shows a case where plaque due to calcification, lipid components, and fibrous components has occurred, and Fig. 2(C) shows a case where plaque due to bleeding has occurred. Note that Fig. 2 shows an example of plaque, and does not limit the properties, area, form, etc. of plaque analyzed by the medical image processing device 100. The properties to be analyzed may be, for example, characteristics related to at least one of hard plaque, soft plaque, stable plaque, unstable plaque, calcification, lipid components, fibrous components, thrombus, and bleeding components.

[0019] Generally, MR images, CT images, ultrasound images, etc. may be used as images for analyzing plaque. FIG. 3 is a diagram for explaining the difference in the way a region including plaque is depicted in an MR image and a CT image. FIG. 3(A) and FIG. 3(B) each show a schematic representation of a blood vessel cross section in a region including plaque in FIG. 2(B) depicted in an MR image and a CT image. FIG. 3(C) and FIG. 3(D) each show a schematic representation of a blood vessel cross section in a region including plaque in FIG. 2(C) depicted in an MR image and a CT image.

[0020] Specifically, as shown in Figures 3(A) and 3(C), in MR images, hemorrhagic and lipid components tend to be depicted with relatively high brightness, followed by fibrous components and calcification, which tend to be depicted with low brightness. Also, as shown in Figures 3(B) and 3(D), in CT images, calcification tends to be depicted with high brightness, followed by fibrous and hemorrhagic components, followed by lipid components, which tend to be depicted with low brightness.

[0021] MR images are generally excellent in contrast discrimination and are good at depicting soft plaque. However, MRI imaging is known to take longer to capture than X-ray CT imaging, and has low image resolution, making it difficult to accurately capture the shape of small plaques and analyze their properties. MR images for analyzing plaque properties are obtained by MRI imaging using, for example, MRA (Magnetic Resonance Angiography) or BB (Black Blood) method.

[0022] On the other hand, CT images generally have a higher image resolution than MR images, and are good at depicting hard plaques. However, CT images have a lot of noise, for example, when X-ray CT imaging is performed at a low dose or at a high resolution. This can make it difficult to easily distinguish plaques from the acquired CT images. In particular, as shown in the lipid and fibrous components in Figure 3(B) and the hemorrhagic components in Figure 3(D), for soft tissues such as unstable plaques, the change in CT value compared to neighboring tissues is small in the energy range used in X-ray CT imaging. In addition, the CT value of the unstable plaque itself tends to be small.

[0023] In addition, the CT value of the plaque is affected by differences in the concentration of contrast medium in tissues adjacent to the plaque, and it may be difficult to determine the composition using only the CT value. Specifically, when phenomena such as beam hardening and blooming artifacts occur, the CT value of tissues adjacent to the plaque becomes high, making it difficult to capture soft tissues due to the effects of artifacts, making it difficult to accurately analyze the properties of the plaque. In addition, because the plaque itself to be imaged is small, the signal-to-noise ratio may decrease, causing variations in the CT value.

[0024] As described above, even if the MR image and the CT image are images of the same region of interest, there are differences in the way they are depicted, such as image resolution and contrast. In the medical image processing device 100 according to the embodiment, the accuracy of analysis of the region and characteristics of the region of interest of the subject is improved in medical diagnosis using MR images and CT images. The configuration and operation of the above functions in the processing circuit 110 of the medical image processing device 100 according to the first embodiment will be described below with reference to the flowchart of FIG. 4 and FIG. 5. Note that FIG. 5 is a diagram for explaining the flow of analyzing plaque with the blood vessel region of the region including plaque in FIG. 2(B) as the region of interest. Note that each diagram in FIG. 5 is a diagram showing a schematic cross-sectional image of the neck.

[0025] In step ST10, the first acquisition function F1 acquires an MR image of the subject acquired by an MRI apparatus. The medical image processing apparatus 100 acquires the MR image of the subject from, for example, the storage circuitry 120. The medical image processing apparatus 100 may also acquire the MR image of the subject from the network interface 140 via a network.

[0026] In step ST20, the second acquisition function F2 acquires a CT image of the subject acquired by the X-ray CT device. The medical image processing device 100 acquires the CT image of the subject from, for example, the storage circuitry 120. The medical image processing device 100 may also acquire the CT image of the subject from the network interface 140 via a network.

[0027] In step ST30, the position correction function F4 corrects the imaging positions of the MR image and the CT image. FIG. 5 shows a schematic diagram of the imaging position of the acquired CT image being corrected to the imaging position of the acquired MR image, but the acquired MR image may be corrected to the imaging position of the acquired CT image. The imaging positions of the CT image and the MR image may be corrected relatively to each other so that they are the same. A known correction method may be used to correct the imaging positions of the MR image and the CT image, and the imaging positions of the CT image and the MR image may be corrected at the same time, such as by enlarging or reducing the size. Note that, if the imaging positions of the acquired MR image and the acquired CT image are the same, step ST30 may be omitted.

[0028] In step ST40, the extraction function F3 extracts a region of interest of the subject from a first image, which is either an MR image or a CT image. As described above, MR images are excellent in contrast discrimination and are good at depicting soft plaque. For example, a case where an area including plaque in the neck in FIG. 5 is extracted as a region of interest of the subject using an MR image as the first image will be described below. The region of interest of the subject can be extracted by a known image analysis algorithm.

[0029] In step ST50, the seed point setting function F5 sets a seed point in the region of interest of the subject in the first image. Setting a seed point means, in other words, selecting a seed point for specifying a point that will be the reference (i.e., starting point) of the analysis in the second image described later. In other words, the seed point is a specific point that becomes useful data in both the first image (MR image in FIG. 5) and the second image (CT image in FIG. 5). The seed point may be a specific point or a specific region. FIG. 5 shows how a seed point is set in a region including plaque extracted as the region of interest of the subject.

[0030] In step ST60, the first analysis function F6 determines the subject's region of interest in a second image, which is the other image of the MR image or the CT image, with high resolution, using the set seed point as a starting point. Specifically, the first analysis function F6 determines the subject's region of interest using a known region determination method such as a region growing method, using the seed point set in the first image as a starting point. By using the seed point set in the first image, it is possible to reduce erroneous detection of the region due to the influence of noise, etc., compared to determining the subject's region of interest from the entire region of the second image without using the seed point. As a result, the subject's region of interest can be determined with high resolution. The subject's region of interest determined with high resolution is also called a high-resolution region of interest. In addition, if the imaging position is corrected in step ST30, the first analysis function F6 analyzes the subject's region of interest with high resolution using the corrected second image.

[0031] In step ST61, the first analysis function F6 analyzes the characteristics of the determined high-resolution region of interest. As shown in Fig. 5, the characteristics analyzed based on the second image are, for example, characteristics of hard plaque, soft plaque, stable plaque, unstable plaque, calcification, lipid component, fibrous component, thrombus, bleeding component, etc.

[0032] In step ST70, the display function F7 displays the determined high-definition region of interest of the subject and the characteristics in the high-definition region of interest. That is, in step ST70 of the first embodiment, the high-definition region of interest based on the second image and the characteristics in the high-definition region of interest are displayed. The high-definition region of interest and the characteristics in the high-definition region of interest are displayed, for example, on the display 150. In addition, the images separated by characteristics may be displayed, or the characteristics may be displayed using different colors, such as green for fibrous components, yellow for lipid components, and red for bleeding components.

[0033] In the medical image processing apparatus 100 according to the first embodiment, seed points that are mutually useful data are set in the first and second images, i.e., in the MR image and the CT image, a region of interest of the subject is determined in high resolution, and the characteristics of the high resolution region of interest are analyzed. Specifically, in Fig. 5, a seed point is set as an MR image having excellent contrast discrimination as the first image, and a CT image having excellent image resolution is set as the second image, and the region of interest is determined in high resolution with the seed point as a starting point.

[0034] According to the medical image processing device 100 of the first embodiment, it is possible to accurately depict the position of a lesion area including hard plaque, soft plaque, etc., and to improve the accuracy of analysis of the area and characteristics of a site of interest in a subject. This leads to easy discovery and identification of the lesion area. In addition, since the area of ​​interest is determined with high resolution, the variation in CT value due to the small lesion area in the second image (CT image in FIG. 5) is suppressed, enabling more accurate analysis and display of characteristics. Furthermore, it also leads to suppression of variation in judgment between radiologists.

[0035] Second embodiment FIG. 6 is a schematic diagram showing an example of the configuration of a medical image processing apparatus 100 according to the second embodiment. As shown in FIG. 6, the processing circuit 110 of the medical image processing apparatus 100 according to the second embodiment is different from the processing circuit 110 of the medical image processing apparatus 100 according to the first embodiment in that the processing circuit 110 of the medical image processing apparatus 100 according to the second embodiment further realizes a position setting function F8 and a second analysis function F9. Hereinafter, the configuration and operation of the above functions in the processing circuit 110 of the medical image processing apparatus 100 according to the second embodiment will be described with reference to the flowchart of FIG. 7 and FIG. 8. Note that the configuration and operation that are not substantially different from the configuration and operation of the medical image processing apparatus 100 according to the first embodiment in FIG. 1 and FIG. 4 are assigned the same reference numerals and will not be described.

[0036] Steps ST10 to ST60 in the second embodiment are not substantially different from steps ST10 to ST60 in the first embodiment, so duplicated explanations will be omitted. In the second embodiment, after step ST60, the process proceeds to step ST62. This does not prevent step ST61 from being performed after step ST60 and then proceeding to step ST62.

[0037] In step ST62, the position setting function F8 sets information on the high-resolution region of interest of the subject determined in the second image by the first analysis function F6 in the first image. As shown in Fig. 8, by setting the high-resolution region of interest of the subject determined in the second image in the first image, it is possible to set the region of interest at a higher resolution than the region of interest extracted in step ST40. When the imaging position is corrected in step ST30, the position setting function F8 uses information on the high-resolution region of interest of the subject determined using the corrected second image.

[0038] In step ST63, the second analysis function F9 determines a high-resolution region of interest again in the first image using information on the set high-resolution region of interest of the subject, and analyzes the characteristics of the high-resolution region of interest. As shown in Fig. 8, the characteristics analyzed based on the first image are, for example, characteristics of hard plaque, soft plaque, stable plaque, unstable plaque, calcification, lipid components, fibrous components, thrombus, bleeding components, etc.

[0039] In step ST70, the display function F7 displays the determined high-definition region of interest of the subject and the characteristics in the high-definition region of interest. That is, in step ST70 of the second embodiment, the high-definition region of interest based on the first image and the characteristics in the high-definition region of interest are displayed. In other respects, step ST70 of the second embodiment is not substantially different from step ST70 of the first embodiment, so that a duplicated description will be omitted.

[0040] According to the medical image processing device 100 of the second embodiment, it is possible to accurately depict the position of a lesion area including hard plaque, soft plaque, etc., and to improve the accuracy of analysis of the area and characteristics of a site of interest in a subject. This leads to easy discovery and identification of the lesion area. In addition, since the area of ​​interest is determined with high resolution, the image resolution in the first image (MR image in FIG. 8) is improved, enabling more accurate analysis and display of characteristics. Furthermore, this also leads to suppression of variability in judgment between radiologists.

[0041] (Third embodiment) FIG. 9 is a schematic diagram showing an example of the configuration of the medical image processing apparatus 100 according to the third embodiment. The processing circuit 110 of the medical image processing apparatus 100 according to the third embodiment is different from the processing circuit 110 of the medical image processing apparatus 100 according to the second embodiment in that the processing circuit 110 of the medical image processing apparatus 100 according to the third embodiment further realizes a third analysis function F10. Hereinafter, the configuration and operation of the above functions in the processing circuit 110 of the medical image processing apparatus 100 according to the third embodiment will be described with reference to the flowchart of FIG. 10 and FIG. 11. Note that the configuration and operation that are substantially the same as those of the medical image processing apparatus 100 according to the first embodiment in FIG. 1 and FIG. 4 and those of the medical image processing apparatus 100 according to the first embodiment in FIG. 7 and FIG. 8 are denoted by the same reference numerals and will not be described.

[0042] Steps ST10 to ST60 of the third embodiment are not substantially different from steps ST10 to ST60 of the first and second embodiments, and therefore overlapping descriptions will be omitted. In the third embodiment, after step ST60, the process proceeds to step ST61. In step ST61, the first analysis function F6 determines a high-resolution region of interest corresponding to the site of interest of the subject in high resolution, and analyzes the properties of the determined high-resolution region of interest. Step ST61 of the third embodiment is not substantially different from step ST61 of the first embodiment, and therefore overlapping descriptions will be omitted.

[0043] In the third embodiment, after step ST61, the process proceeds in order to step ST62 and step ST63. In step ST62, the position setting function F8 sets information on the high-resolution region of interest of the subject determined in the second image by the first analysis function F6 to the first image. In addition, in step ST63, the second analysis function F9 determines the high-resolution region of interest again using information on the high-resolution region of interest of the subject set in the first image, and analyzes the properties in the high-resolution region of interest. Steps ST62 and ST63 of the third embodiment are not substantially different from steps ST62 and ST63 of the second embodiment, so that overlapping explanations will be omitted. In the third embodiment, the process proceeds to step ST64 after step ST63.

[0044] In step ST64, the third analysis function F10 performs a fusion analysis of the property in the high-resolution region of interest of the subject analyzed by the first analysis function F6 and the property in the high-resolution region of interest of the subject analyzed by the second analysis function F9, and generates a new property distribution map in the high-resolution region of interest. Specifically, the property in the high-resolution region of interest analyzed based on the first image (MR image in FIG. 11) is fused with the property in the high-resolution region of interest analyzed based on the second image (CT image in FIG. 11) to generate a new property distribution map.

[0045] In step ST71, the display function F7 displays the attribute distribution map obtained by the fusion analysis. The attribute distribution map obtained by the fusion analysis is displayed, for example, on the display 150. In addition, the images may be displayed separately for each attribute, or may be displayed using different colors for each attribute, such as green for fibrous components, yellow for lipid components, and red for hemorrhagic components.

[0046] According to the medical image processing device 100 of the third embodiment, a high-resolution region of interest is determined and the characteristics of the high-resolution region of interest are analyzed while compensating for data in which the first image (an MR image in FIG. 11) and the second image (a CT image in FIG. 11) are weak. Therefore, while maintaining the effects of the first and second embodiments, the accuracy of analysis of the region and characteristics of the subject's area of ​​interest can be further improved.

[0047] (others) In the medical image processing apparatus 100 according to the first, second, and third embodiments, the first image is an MR image and the second image is a CT image. However, in the medical image processing apparatus 100 according to the embodiment, the first image may be a CT image and the second image may be an MR image. As described above, in general, CT images are good at depicting hard plaque, and MR images are good at depicting soft plaque. Therefore, by setting a region in the CT image that is good at depicting as a seed point, it is possible to improve the analysis accuracy of the region and properties of the site of interest of the subject.

[0048] According to at least one of the embodiments described above, it is possible to improve the accuracy of analyzing the region and properties of a site of interest in a subject in medical diagnosis using MR images and CT images.

[0049] In the above embodiment, the term "processor" refers to a circuit such as a dedicated or general-purpose CPU (Central Processing Unit), GPU (Graphics Processing Unit), or an Application Specific Integrated Circuit (ASIC), a programmable logic device (e.g., a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and an FPGA). The processor realizes various functions by reading and executing a program stored in a storage medium.

[0050] In addition, although the above embodiment shows an example in which a single processor of a processing circuit realizes each function, a processing circuit may be configured by combining multiple independent processors, and each processor may realize each function. In addition, when multiple processors are provided, a storage medium for storing a program may be provided separately for each processor, or a single storage medium may collectively store programs corresponding to the functions of all the processors.

[0051] In addition, the first acquisition function F1, the second acquisition function F2, the extraction function F3, the position correction function F4, the seed point setting function F5, the first analysis function F6, the display function F7, the position setting function F8, the second analysis function F9, and the third analysis function F10 in the description of the embodiments are examples of the first acquisition unit, the second acquisition unit, the extraction unit, the position correction unit, the seed point setting unit, the first analysis unit, the display unit, the position setting unit, the second analysis unit, and the third analysis unit, respectively, in the description of the claims.

[0052] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as the scope and spirit of the invention. [Explanation of symbols]

[0053] REFERENCE SIGNS LIST 100... medical image processing device 200... X-ray CT device 300... MRI device 110... processing circuit 120... memory circuit 140... network interface F1... first acquisition function F2... second acquisition function F3... extraction function F4... position correction function F5... seed point setting function F6... first analysis function F7... display function F8... position setting function F9... second analysis function F10... third analysis function

Claims

1. a first acquisition unit that acquires an MR image of a subject acquired by an MRI apparatus; A second acquisition unit that acquires a CT image of the subject acquired by an X-ray CT device; an extraction unit that extracts a region of interest of the subject from a first image, which is either the MR image or the CT image; a setting unit that sets a seed point in the region of interest of the subject in the first image; a first analysis unit that determines the region of interest of the subject with high resolution in a second image that is the other image different from the one of the MR image and the CT image, using the set seed point as a starting point; A medical image processing device comprising:

2. the first image is the MR image and the second image is the CT image; The medical image processing device according to claim 1 .

3. a position correction unit that corrects the imaging positions of the MR image and the CT image, The first analysis unit analyzes the region of interest of the subject with high resolution using the corrected second image. The medical image processing device according to claim 1 .

4. The first analysis unit further determines a high-resolution region of interest corresponding to the site of interest of the subject, and analyzes properties of the determined high-resolution region of interest. The medical image processing device according to claim 1 .

5. a position setting unit that sets, in the first image, information on a high-definition region of interest corresponding to the site of interest of the subject determined in the second image by the first analysis unit; and a second analysis unit that uses information on the set high-resolution region of interest of the subject to determine a high-resolution region of interest again in the first image and analyzes a property in the high-resolution region of interest. The medical image processing device according to claim 1 .

6. The region of interest of the subject includes at least one of a stenosis region or a plaque region. The medical image processing device according to claim 1 .

7. The characteristics analyzed are characteristics related to at least one of hard plaque, soft plaque, stable plaque, unstable plaque, calcification, lipid component, fibrous component, thrombus, and hemorrhage component. The medical image processing device according to claim 4 or 5.

8. a display unit that displays the determined high-resolution region of interest of the subject and the property in the high-resolution region of interest, The medical image processing device according to claim 4 or 5.

9. The apparatus further includes a position setting unit, a second analysis unit, and a third analysis unit, The first analysis unit further determines a high-resolution region of interest corresponding to the site of interest of the subject, and analyzes a property of the determined high-resolution region of interest; the position setting unit sets information of the high-resolution region of interest of the subject determined in the second image by the first analysis unit, in the first image; The second analysis unit determines a high-resolution region of interest again using information of the high-resolution region of interest of the subject set in the first image, and analyzes a property in the high-resolution region of interest; The third analysis unit performs a fusion analysis of the property in the high-resolution region of interest of the subject analyzed by the first analysis unit and the property in the high-resolution region of interest of the subject analyzed by the second analysis unit, and generates a new property distribution map in the high-resolution region of interest. The medical image processing device according to claim 1 .

10. A display unit for displaying the property distribution map obtained by the fusion analysis is further provided. The medical image processing apparatus according to claim 9 .

11. An MRI image of the subject is obtained by an MRI device; acquiring a CT image of the subject obtained by an X-ray CT apparatus; extracting a site of interest of the subject from a first image, which is either the MR image or the CT image; setting a seed point in the first image at the region of interest of the subject; determining the region of interest of the subject with high resolution in a second image, the second image being different from the one of the MR image and the CT image, using the set seed point as a starting point; Medical image processing method.

12. acquiring an MR image of the subject by an MRI device; acquiring a CT image of the subject using an X-ray CT apparatus; extracting a region of interest of the subject from a first image, which is either the MR image or the CT image; setting a seed point in the first image at the region of interest of the subject; determining the region of interest of the subject with high resolution in a second image, the second image being different from the one of the MR image and the CT image, using the set seed point as a starting point; A medical image processing program that allows a computer to execute the above.

Images