Image processing method, program, and image processing device

Abstract

This image processing method includes: a step in which a vascular mask creating unit creates a vascular mask image by subjecting an image, obtained from a magnetic resonance signal obtained by enhancing regions corresponding to blood vessels in an MRI image, to removal of dot-shaped low signals, removal of linear low signals, extraction of linear low signals, removal of dot-shaped high signals, removal of linear high signals, and extraction of linear high signals; and a step in which a mask processing unit generates an image obtained by employing the vascular mask image to remove a vascular structure from a phase difference enhanced image created from the MRI image.

Description

[Technical Field]

[0001] The present invention relates to an image processing method, a program, and an image processing device. This application claims priority based on Japanese Patent Application No. 2021-029161, filed on February 25, 2021, the contents of which are incorporated herein by reference. [Background technology]

[0002] Amyloid-β is the causative agent of Alzheimer's disease, the leading cause of dementia. Because amyloid-β deposition in the brains of Alzheimer's disease patients can begin as early as 20 to 30 years before the onset of dementia, detecting amyloid-β during the asymptomatic stage is highly desirable for early treatment, prevention, and drug development monitoring. A conventional technique for diagnosing amyloid-β deposition in the brain is positron emission tomography (PET) (amyloid-PET), which uses radioactive agents that bind to amyloid-β, such as PiB (Pittsburgh compound-B). While amyloid-PET is a useful imaging test that can visualize amyloid-β deposition in the brain, it has drawbacks, such as patient exposure, high testing costs, and limited availability. Therefore, extensive research is being conducted on predicting cerebral amyloid-β deposition using magnetic resonance imaging (MRI), a non-radiation-related diagnostic method. For example, research is being conducted to predict the deposition of amyloid beta and age-related iron in the brain based on quantitative values ​​called QSM (Quantitative Susceptibility Mapping) obtained from phase component information of MRI images. Also, a method has been proposed in which phases are selected and enhanced according to the tissue contrast to be enhanced using PADRE (Patent Document 1, for example). The PADRE method can enhance tissue resolution of target tissues, such as amyloid plaques and blood vessels.

[0003] Furthermore, visual assessment using AP-PADRE (Amyloid Plaque-PADRE) has been proposed. It has been reported that the distribution of low signals in the cerebral cortex of AP-PADRE shows a significant difference in visual assessment between patients clinically diagnosed with Alzheimer's disease (patients with Alzheimer's dementia) and control groups, and that the distribution of low signals in the superior temporal gyrus in Alzheimer's disease patients is positively correlated with the Mini-Mental State Examination (MMSE) score (see, for example, Non-Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2010 / 073923 Brochure [Non-patent literature]

[0005] [Non-Patent Document 1] Tateishi M. et al., “Differentiating between Alzheimer Disease Patients and Controls with Phase-difference-enhanced Imaging at 3T: A Feasibility Study”, Magn Reson Med Sci. 2018 Summary of the Invention [Problem to be solved by the invention]

[0006] However, although the evaluation in Non-Patent Document 1 involved visual assessment, it did not extract and comparatively evaluate amyloid beta, the cause of Alzheimer's disease, but rather relied solely on clinical symptoms to assess discrimination ability. Furthermore, methods using AP-PADRE images make it difficult to distinguish between PiB-positive and PiB-negative cases based solely on the simple distribution of low signal intensity in the cortex. Note that PiB-positive refers to a positive diagnosis in PiB-PET testing, while PiB-negative refers to a negative diagnosis in PiB-PET testing. Furthermore, although PiB-positive cases tend to exhibit unclear corticomedullary boundaries and regional differences in signal intensity in the created color map, these findings were not reliable. Thus, visual assessment of amyloid beta deposition was difficult using conventional techniques.

[0007] The present invention has been made in consideration of the above-mentioned problems, and aims to provide an image processing method, program, and image processing device that can visually determine and quantitatively evaluate the presence or absence of amyloid beta deposition, and diagnose the presence or absence of Alzheimer's disease. [Means for solving the problem]

[0008] (1) In order to achieve the above object, an image processing method according to one embodiment of the present invention includes the steps of: a vascular mask creation unit creating a vascular mask image by removing point-like low signals, removing linear low signals, and extracting linear low signals, and removing point-like high signals, removing linear high signals, and extracting linear high signals from an image obtained from a magnetic resonance signal that has enhanced areas corresponding to blood vessels in an MRI image; and a mask processing unit using the vascular mask image to generate an image from which vascular structures have been removed from a phase-contrast-enhanced image created from the MRI image.

[0009] (2) Furthermore, the image processing method according to one aspect of the present invention may further include a step in which a separation unit separates local low signal intensity and diffuse components from the image from which the vascular structure has been removed, and a step in which a brain surface projection image output unit projects the diffuse components onto the brain surface to generate a brain surface projection image.

[0010] (3) In addition, in an image processing method according to one aspect of the present invention, the vascular mask creation unit may create the vascular mask image by morphological calculation processing, and the separation unit may separate the local low signal intensity and diffuse component image by the morphological calculation processing.

[0011] (4) In addition, in an image processing method according to one aspect of the present invention, the vascular mask creation unit may include the steps of: removing the point-like low signals by morphological calculation processing using a 3×3×3 linear kernel in 13 directions; removing the linear low signals by morphological calculation processing using a 3×3×3 planar kernel in 13 directions on the image from which the point-like low signals have been removed; extracting the linear low signals using the image from which the point-like low signals have been removed and the image from which the linear low signals have been removed; removing the point-like hyperintensities by morphological calculation processing using a 3×3×3 linear kernel in 13 directions; removing the linear hyperintensities by morphological calculation processing using a 3×3×3 planar kernel in 13 directions on the image from which the point-like hyperintensities have been removed; and extracting the linear hyperintensities using the image from which the point-like hyperintensities have been removed and the image from which the linear hyperintensities have been removed.

[0012] (5) In addition, in an image processing method according to one aspect of the present invention, the vascular mask creation unit may perform, for different pixel widths, a process of removing the point-like low signals, a process of removing the linear low signals, a process of extracting the linear low signals, a process of removing the point-like high signals, a process of removing the linear high signals, and a process of extracting the linear high signals.

[0013] (6) In addition, in an image processing method according to one aspect of the present invention, the separation unit may perform a closing process by morphological operation using a spherical kernel of radius p (p is an integer of 1 to n) pixels on the image from which the vascular structure has been removed, to separate the local low signal of size n, and separate a diffuse image using p results obtained by performing the closing process on the image from which the vascular structure has been removed using a spherical kernel of radius n pixels.

[0014] (7) In addition, in the image processing method according to one aspect of the present invention, the phase-contrast-weighted image may be an AP-PADRE image created by applying amyloid beta-bound iron-phase-contrast-weighted imaging to the MRI image.

[0015] (8) In the image processing method according to one aspect of the present invention, the vascular mask creating unit may extract information about a region of interest using the vascular mask image.

[0016] (9) In addition, in an image processing method according to one aspect of the present invention, the vascular mask creation unit may extract information about the region of interest by performing mask processing based on the vascular mask image, a template of the region of interest, information obtained by aligning the acquired enhanced image with the phase-contrast enhanced image to align both images, and information obtained by converting standardized information into the individual brain coordinates based on information obtained by transforming the enhanced image by nonlinear transformation and aligning it with a template image of the enhanced image on standard brain coordinates.

[0017] (10) In order to achieve the above object, a program according to one embodiment of the present invention causes a computer to create a vascular mask image by removing point-like low signals, removing linear low signals, and extracting linear low signals, and removing point-like high signals, removing linear high signals, and extracting linear high signals from an image obtained from a magnetic resonance signal that has enhanced areas corresponding to blood vessels in an MRI image, and then uses the vascular mask image to generate an image from a phase-contrast-enhanced image created from the MRI image in which vascular structures have been removed.

[0018] (11) In order to achieve the above object, an image processing device according to one embodiment of the present invention includes a vascular mask creation unit that creates a vascular mask image by removing point-like low signals, removing linear low signals, and extracting linear low signals, and removing point-like high signals, removing linear high signals, and extracting linear high signals from an image obtained from a magnetic resonance signal that has enhanced areas corresponding to blood vessels in an MRI image, and a mask processing unit that uses the vascular mask image to generate an image from which vascular structures have been removed from a phase-contrast-enhanced image created from the MRI image.

[0019] (12) In the image processing device according to one aspect of the present invention, the vascular mask creating unit may extract information about a region of interest using the vascular mask image. [Effects of the Invention]

[0020] According to the present invention, it is possible to visually determine and quantitatively evaluate the presence or absence of amyloid β deposition, and to diagnose the presence or absence of Alzheimer's disease. [Brief explanation of the drawings]

[0021] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of an image processing apparatus according to an embodiment. [Figure 2] FIG. 1 is a diagram showing a two-component model for explaining an overview of AP-PADRE. [Figure 3] FIG. 10 is a diagram illustrating a morphological operation. [Figure 4] 10A to 10C are diagrams illustrating an example of a process for extracting a blood vessel structure according to an embodiment. [Figure 5] FIG. 10 is a diagram showing an example of extraction for each blood vessel diameter. [Figure 6] 10A and 10B are diagrams illustrating an example of a process for creating a blood vessel mask image according to the embodiment. [Figure 7] This figure shows an example of an image before applying a vascular mask image according to the embodiment, and an example of an image in which vascular structures have been removed from an AP-PADRE image using the vascular mask, and in which extraparenchymal components have been removed using a brain parenchyma mask created from MPRAGE. [Figure 8] 10 is a flowchart of a vascular mask image creation procedure according to an embodiment. [Figure 9] 10 is a flowchart of a processing procedure for removing point-like low signals in one pixel width according to an embodiment. [Figure 10] 10 is a flowchart of a processing procedure for removing linear low signal intensity in one pixel width according to an embodiment. [Figure 11] FIG. 13 shows a 3×3×3 pixel linear kernel in 13 directions. [Figure 12] FIG. 13 shows a 3×3×3 pixel planar kernel in 13 directions. [Figure 13] FIG. 1 shows PADRE images from a PiB-negative group in a comparative example, from which vascular structures and extra-brain parenchymal components have been removed, and an example of an image used for comparison with PiB-PET. [Figure 14] FIG. 1 shows PADRE images from which vascular structures and extracerebral parenchymal components of a PiB-positive group have been removed in a comparative example, and an example of an image used for comparison with PiB-PET. [Figure 15] An example of an AAL atlas image set on standard brain coordinates, and an example of an AAL atlas ROI overlaid on a PADRE image. [Figure 16] This figure shows the average values ​​of PADRE signals normalized to the average value of the cerebellum for the PiB-positive and -negative groups in the cortex analyzed by ROI using the AAL atlas. [Figure 17] This figure shows the average values ​​of PADRE signals normalized to the average value of the cerebellum for the PiB-positive and -negative groups in the cortex analyzed by ROI using the AAL atlas. [Figure 18]This figure shows the average values ​​of PADRE signals normalized to the average value of the cerebellum for the PiB-positive and -negative groups in the cortex analyzed by ROI using the AAL atlas. [Figure 19] This figure shows regions in the brain where there were statistically significant differences in the mean values ​​of the normalized PADRE signals between the PiB-positive and -negative groups in the cortex, as determined by ROI analysis using the AAL atlas, and visualized in three dimensions by mapping them onto the brain surface. [Figure 20] This figure shows color-coded histograms of PiB-positive and -negative PADRE signals for voxel values ​​contained in local brain cortex analyzed by ROI using the AAL atlas. [Figure 21] 10 is a flowchart of a process for separating a local low signal component and a diffuse component according to an embodiment. [Figure 22] FIG. 10 is a diagram showing the average values ​​of normalized PADRE signal values ​​in the ROI of the diffuse component image for the PiB-positive group and the PiB-negative group. [Figure 23] FIG. 10 is a diagram showing the average values ​​of normalized PADRE signal values ​​in the ROI of the diffuse component image for the PiB-positive group and the PiB-negative group. [Figure 24] FIG. 10 is a diagram showing the average values ​​of normalized PADRE signal values ​​in the ROI of the diffuse component image for the PiB-positive group and the PiB-negative group. [Figure 25] FIG. 10 is a diagram showing the average values ​​of normalized PADRE signal values ​​in the ROI of the diffuse component image for the PiB-positive group and the PiB-negative group. [Figure 26] FIG. 10 shows example images of mean values ​​of diffuse components extracted from PADRE images projected onto the brain surface in the PiB-negative and -positive groups. [Figure 27] This is an image in which blood vessels and low signals are removed from an MRI image of a PiB-negative patient with spinocerebellar degeneration using the method of the embodiment, and the extracted diffuse components are mapped onto the brain surface. [Figure 28] This is an image in which blood vessels and low signals are removed from an MRI image of a healthy (NC) PiB-negative individual using the method of the embodiment, and the extracted diffuse components are mapped onto the brain surface. [Figure 29]This is an image in which blood vessels and low signals are removed from an MRI image of a PiB-positive patient with mild cognitive impairment using the method of the embodiment, and the extracted diffuse components are mapped onto the brain surface. [Figure 30] This is an image in which blood vessels and low signals are removed from an MRI image of a PiB-positive patient with mild cognitive impairment using the method of the embodiment, and the extracted diffuse components are mapped onto the brain surface. [Figure 31] FIG. 31 shows the results of interpretation of 11 subjects, including FIGS. 27 to 30. [Figure 32] FIG. 10 shows a punctate component image and a diffuse component image created from an AP-PADRE image of a patient with MCI due to Alzheimer's disease. [Figure 33] The difference in the mean signal between the PiB-positive and PiB-negative groups was calculated for the cerebellum-normalized AP-PADRE punctate component image in each ROI, and the result was color-mapped projected onto the brain surface. [Figure 34] The difference in the mean signal between the PiB-positive and PiB-negative groups was calculated for the cerebellum-normalized AP-PADRE diffuse component image in each ROI, and the result was color-mapped onto the brain surface. [Figure 35] FIG. 10 shows the results of the non-AD group versus the MCI+Alzheimer's dementia group in the fifth validation. [Figure 36] FIG. 10 shows the results of the non-AD group versus the MCI+Alzheimer's dementia group in the fifth validation. [Figure 37] FIG. 10 shows the results of the non-AD group versus the MCI+Alzheimer's dementia group in the fifth validation. [Figure 38] FIG. 10 shows the results of a one-tailed t-test between the non-AD group and the Alzheimer's disease group in the fifth validation. [Figure 39] FIG. 10 shows the results of a one-tailed t-test between the non-AD group and the Alzheimer's disease group in the fifth validation. [Figure 40] FIG. 10 shows the results of a one-tailed t-test between the non-AD group and the Alzheimer's disease group in the fifth validation. [Figure 41]FIG. 10 shows the results of a one-tailed t-test of similar ROI analysis results in two groups, PiB-negative and -positive, for 11 cases with PiB data in the fifth validation. [Figure 42] FIG. 10 shows the results of a one-tailed t-test of similar ROI analysis results in two groups, PiB-negative and -positive, for 11 cases with PiB data in the fifth validation. [Figure 43] FIG. 10 shows the results of a one-tailed t-test of similar ROI analysis results in two groups, PiB-negative and -positive, for 11 cases with PiB data in the fifth validation. DETAILED DESCRIPTION OF THE INVENTION

[0022] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the drawings used in the following description, the scale of each component is appropriately changed so that each component can be recognized.

[0023] Fig. 1 is a diagram showing an example of the configuration of an image processing device 1 according to this embodiment. As shown in Fig. 1, the image processing device 1 includes an image acquisition unit 11, a vascular-weighted image generation unit 12, a bias field correction unit 13, a vascular mask creation unit 14, an AP-PADRE image generation unit 15, a bias field correction unit 16, a mask processing unit 17, a separation unit 18, a brain surface projection image output unit 19, a 3D-T1-weighted image generation unit 20, a registration unit 21, an anatomical standardization unit 22, a coordinate conversion unit 23, a statistical image analysis result output unit 24, a template storage unit 25, a coordinate conversion unit 26, a mask processing unit 27, and a region-of-interest analysis result output unit 28.

[0024] The image processing device 1 performs image processing on the acquired MRI image, and outputs a brain surface projection image, a statistical image analysis result, and a region of interest analysis result.

[0025] The image acquisition unit 11 acquires two types of images: an original PADRE image based on an MRI image of a patient obtained from magnetic resonance signals, and an original image for generating a 3D-T1 weighted image. The image acquisition unit 11 outputs the acquired original PADRE image to the vascular-weighted image generation unit 12 and the AP-PADRE image generation unit 15, and outputs the acquired original image for generating a 3D-T1 weighted image to the 3D-T1 weighted image generation unit 20. The image acquisition unit 11 may acquire the vascular-weighted image, the AP-PADRE image, and the 3D-T1 weighted image from an external device.

[0026] The vascular image generator 12 generates a vascular image by enhancing the vascular portion of the original PADRE image using a susceptibility change in the image, for example, using a reconstruction method such as SWI (Susceptibility Weighted Imaging). When the image acquirer 11 acquires a vascular image, the vascular image generator 12 outputs the vascular image output by the image acquirer 11 to the bias field corrector 13.

[0027] The bias field correction unit 13 performs bias field correction on the vessel-enhanced image using information output by the alignment unit 21. Note that the bias field correction is a correction for uniforming the influence of a magnetic field.

[0028] The vascular mask creating unit 14 creates a vascular mask image for removing the vascular structure from the bias field corrected vascular enhancement image. The method for creating the vascular mask image will be described later.

[0029] The AP-PADRE image generator 15 generates an AP-PADRE image from the original PADRE image by a reconstruction method. When the image acquirer 11 acquires an AP-PADRE image, the AP-PADRE image generator 15 outputs the AP-PADRE image output by the image acquirer 11 to the bias field corrector 16.

[0030] The bias field correction unit 16 obtains a corrected image by performing bias field correction on the AP-PADRE image using information output by the registration unit 21. At the same time, a gray matter component image is obtained by tissue segmentation.

[0031] The mask processing unit 17 applies the gray matter mask image obtained by binarizing the gray matter image output by the Bias Field correction unit 16 and the vascular mask image created by the vascular mask creation unit 14 to the Bias Field corrected image output by the Bias Field correction unit 16, and removes parts corresponding to blood vessels and parts other than gray matter from the image.

[0032] The separation unit 18 separates the image from which the blood vessels have been removed into local low signal components and diffuse components. The separation process will be described later.

[0033] The brain surface projection image output unit 19 outputs the brain surface projection image to an external device (for example, an image display device, a printing device, etc.) based on the result of separation by the separation unit 18.

[0034] The 3D-T1-weighted image generating unit 20 generates a T1-weighted image including the whole brain region by, for example, imaging using the gradient echo method. The 3D-T1-weighted image is used for the mutual registration with PADRE images (vascular-weighted images and AP-PADRE images) and the anatomical standardization process of the image. When the image acquiring unit 11 acquires a 3D-T1-weighted image, the 3D-T1-weighted image generating unit 20 outputs the 3D-T1-weighted image output by the image acquiring unit 11 to the registration unit 21 and the anatomical standardization unit 22.

[0035] The registration unit 21 registers the 3D-T1 weighted image to the AP-PADRE image, and aligns the two images.

[0036] The anatomical standardization unit 22 transforms the 3D-T1 weighted image by nonlinear transformation and aligns it with a 3D-T1 template image (T1 weighted image template) on the standard brain coordinates, thereby obtaining a transformation vector field between the individual brain coordinates and the standard brain coordinates.

[0037] The coordinate conversion unit 23 converts the information output by the separation unit 18 into standard brain coordinates based on the information output by the anatomical standardization unit 22 .

[0038] The statistical image analysis result output unit 24 performs a voxel-based statistical image analysis using a known method based on the information output by the coordinate transformation unit 23, and outputs the analysis results to an external device.

[0039] The template storage unit 25 stores templates of regions of interest. The templates of regions of interest are used, for example, in ROI analysis, which will be described later.

[0040] The coordinate conversion unit 26 converts the standardized information into individual brain coordinates based on the template of the region of interest stored in the template storage unit 25, the information output by the alignment unit 21, and the information output by the anatomical standardization unit 22.

[0041] The mask processing unit 27 extracts information about the region of interest by performing mask processing based on the vascular mask image created by the vascular mask creating unit 14 and the information output by the coordinate transformation unit 26.

[0042] The region of interest analysis result output unit 28 analyzes the region of interest using a known method based on the information output by the mask processing unit 27, and outputs the analysis result to an external device.

[0043] 1 is an example and is not limited to this. The image processing device 1 does not necessarily have to include, for example, the 3D-T1 weighted image generating unit 20, the registration unit 21, the anatomical standardization unit 22, the coordinate conversion unit 23, the statistical image analysis result output unit 24, the template storage unit 25, the coordinate conversion unit 26, the mask processing unit 27, and the region of interest analysis result output unit 28.

[0044] (AP-PADRE) Here, we will explain the outline of AP-PADRE. Figure 2 shows a two-component model for explaining the outline of AP-PADRE. In Figure 2, the horizontal axis represents phase (rad), and the vertical axis represents the number of voxels (frequency of distribution). The dashed line g31 represents the phase distribution of phase image data collected from a region of interest (ROI). The dashed line g32 represents the phase distribution of iron deposits (age-related iron) that accumulate physiologically with aging, which is a background component relative to the phase distribution of iron (amyloid iron) in the AP, which is the target of detection. The dashed line g33 represents the phase distribution of iron accumulated in the AP, and the threshold g34 represents the phase threshold. Each open square (□) represents measured data. The threshold g34 represents the phase value at the intersection of the distribution of the dashed line g31 and the distribution of the dashed line g33. At phase values ​​lower than the intersection, the iron distribution in the AP becomes dominant. Moreover, as shown in Figure 2, the entire phase distribution is mostly occupied by the negative region below phase value 0, and furthermore, the phase distribution of the AP is biased towards the negative side (to the left) of the overall distribution. Note that the two-component model assumes that the phase distribution within the ROI is the sum of the phase distribution of iron corresponding to the background component and the phase distribution of iron accumulated in the AP.

[0045] First, an overview of PADRE (phase contrast enhanced imaging) will be provided. PADRE is a post-processing technique based on conventional SWI (susceptibility weighted imaging), which enhances the imaging capability of target tissues by selecting and enhancing the phase corresponding to the tissue to be enhanced. By using PADRE, target tissues, such as tissues, blood vessels, and amyloid beta, can be enhanced by improving the contrast in MRI head images (see Patent Document 1).

[0046] Next, the AP-PADRE used in this embodiment will be described. AP-PADRE is an image that emphasizes amyloid plaques. AP-PADRE is an image reconstruction method that enables visualization of amyloid-β accumulation by selecting the phase difference specific to iron that polymerizes with amyloid-β protein, one of the causes of Alzheimer's disease, and enhancing that contrast. Iron present in the cerebral cortex is classified into physiological iron, including age-related iron, and non-physiological iron, including amyloid-associated iron. AP-PADRE emphasizes and visualizes amyloid-β-bound iron by utilizing the difference in phase difference.

[0047] Visual assessment using AP-PADRE revealed significant differences in the distribution of hypointensities in the cerebral cortex between patients with Alzheimer's disease and control groups. Furthermore, visual assessment using AP-PADRE has been reported to positively correlate the distribution of hypointensities in the superior temporal gyrus in patients with Alzheimer's disease with MMSE (see Non-Patent Document 1). Note that this assessment was a clinical evaluation and did not evaluate actual amyloid beta. PiB-PET is an imaging technique that can visualize amyloid beta accumulation in the brain. Visual assessments of AP-PADRE and PiB-PET in patients with Alzheimer's disease, mild cognitive impairment, healthy individuals, and non-Alzheimer's disease patients revealed that it was difficult to distinguish between PiB-positive and PiB-negative cases based solely on the simple distribution of hypointensities in the cortex using AP-PADRE images. Patients with amyloid beta accumulation in the brain are referred to as having "Alzheimer's disease," while patients exhibiting symptoms of dementia due to amyloid beta accumulation are referred to as having "Alzheimer's dementia."

[0048] The poor contrast within the brain parenchyma of AP-PADRE images, which is thought to be a factor in the challenges of clinical diagnosis using AP-PADRE images, is thought to be due in part to the fact that the numerous fine vascular structures running near the cerebral cortex, the region of interest, are depicted as significantly low signal intensity in AP-PADRE images. Furthermore, patchy low signal intensity structures are observed within the cortex in AP-PADRE images, but it was unclear whether these patchy structures or the signal value of the cortex itself, which serves as the background, reflect amyloid-β deposition. For this reason, in this embodiment, image processing is performed to remove vascular structures that interfere with quantitative evaluation and to separate the patchy structures and diffuse components of the cortex.

[0049] (Extraction and removal of blood vessel structures) Next, the process of removing blood vessel structures that hinder quantitative evaluation will be described with reference to FIGS. FIG. 3 is a diagram illustrating morphological operations. FIG. 4 is a diagram illustrating an example of a vascular structure extraction process according to this embodiment. In this embodiment, for example, morphological operations are used to extract the linear structure of blood vessels. As shown in FIG. 3, morphological transformation performs image processing such as "dilation" and "erosion" on a grayscale image. In morphological transformation, two inputs are provided: an input image and a structuring element (kernel). Basic morphological processes include erosion (g52, g61) and dilation (g51, g62), as well as opening and closing processes g50 and g60, which combine these two processes. Furthermore, the morphological transformation includes a black-hat transformation process g71 that calculates the difference between the input image g61 and the closing-processed image g53, and a top-hat transformation process g72 that calculates the difference between the input image g61 and the opening-processed image g63. As described below, a linear kernel is used for removing point components, and a planar kernel is used for removing linear components. 3 are merely examples, and the values ​​of the pixels are not limited to these. The separating unit 18 uses the erosion, dilation, closing, and black hat techniques described with reference to FIG.

[0050] The image processing device 1 removes noise in the background of the input image through the processing of Figure 3 and fills in small black dots contained within the object, and then extracts a linear structure g84 by extracting point components g82 from the input image g81 using a linear kernel and extracting line components g83 using a planar kernel, as shown in Figure 4.

[0051] Next, the process of creating a blood vessel mask image by morphological calculation will be described with reference to FIGS. FIG. 5 shows an example of extraction for each blood vessel diameter. Image g101 is an image obtained by calculation with a 3-pixel width, image g102 is an image obtained by calculation with a 4-pixel width, and image g103 is an image obtained by calculation with a 5-pixel width. In this way, by changing the pixel width used for calculation, blood vessel images to be removed from the image according to the blood vessel diameter can be extracted and removed. Note that a low signal intensity is an intensity that appears low in an enhanced image (black in the image). Also, a high signal intensity is an intensity that appears high in an enhanced image (white in the image). Note that the pixel width shown in FIG. 5 is an example and is not limited to this, and may be 6 or more pixels wide.

[0052] FIG. 6 is a diagram showing an example of a process for creating a vascular mask image according to this embodiment. As shown in FIG. 6, the image processing device 1 first performs binarization processing g122 on an input image g121. The threshold value used in the binarization processing is set based on the volume ratio to the intracranial volume. The volume ratio of the arterial and venous parts within the cranium is approximately 2% of the intracranial volume, and the threshold value is set based on this. In this embodiment, the mask processing unit 17 creates a vascular mask image including a margin and removes the vascular image.

[0053] Next, the image processing device 1 performs expansion processing g124 (g125) on the binarized image g123 and removes the vascular structure from the MRI image using a vascular mask image such as that shown in Figure 7. Figure 7 shows an image g121 before applying the vascular mask image according to this embodiment, and an image g126 obtained by removing the vascular structure from the AP-PADRE image using the vascular mask and then removing extraparenchymal components using a brain parenchyma mask created from MPRAGE. Note that for visual evaluation, image g126, which includes both gray and white matter, is used. For quantitative analysis, only the gray matter portion is used as the ROI. By removing the vascular structure from the MRI image as shown in Figure 7, it becomes possible to visually evaluate and analyze low-signal structures within the cortex.

[0054] (Vascular mask creation procedure) Next, an example of a procedure for creating a vascular mask will be described. FIG. 8 is a flowchart of the vascular mask image creation procedure according to this embodiment.

[0055] (Step S11) The vascular mask creating unit 14 removes one-pixel-wide dotted low signals from the bias field corrected vascular image. The processing method for removing low signals will be described with reference to FIG.

[0056] (Step S12) The vascular mask creation unit 14 removes linear hypointensities of one pixel width from the image from which point hypointensities of one pixel width have been removed, and extracts linear hypointensities of one pixel width. The processing method for removing and extracting linear hypointensities will be described with reference to FIG. 10.

[0057] (Step S13) The vascular mask creating unit 14 removes a 2-pixel wide point hypointensity from the image from which the 1-pixel wide point hypointensity and the 1-pixel wide linear hypointensity have been removed.

[0058] (Step S14) The vascular mask creation unit 14 removes the 2-pixel-wide linear hypointensity signal from the image in which the 1-pixel-wide point-like hypointensity signal, the linear hypointensity signal, and the 2-pixel-wide point-like hypointensity signal have been removed, and extracts the 2-pixel-wide linear hypointensity signal.

[0059] (Step S15) The vascular mask creation unit 14 removes a 3-pixel wide point-like low signal intensity from the image in which the 1-pixel wide point-like low signal intensity, the 1-pixel wide linear low signal intensity, the 2-pixel wide point-like low signal intensity, and the 2-pixel wide linear low signal intensity have been removed.

[0060] (Step S16) The vascular mask creation unit 14 removes the 3-pixel-wide linear hypointensity signal from the image in which the 1-pixel-wide point-like hypointensity signal, the 1-pixel-wide linear hypointensity signal, the 2-pixel-wide point-like hypointensity signal, the 2-pixel-wide linear hypointensity signal, and the 3-pixel-wide point-like hypointensity signal have been removed, and extracts the 3-pixel-wide linear hypointensity signal.

[0061] (Step S17) The vascular mask creation unit 14 creates a linear low signal intensity image using the extracted 1-pixel wide linear low signal intensity extraction image, the 2-pixel wide linear low signal intensity extraction image, and the 3-pixel wide linear low signal intensity extraction image. Through the processing of steps S11 to S17, the vascular mask creation unit 14 extracts low signal intensity components, mainly venous components.

[0062] (Step S18) The vascular mask creation unit 14 performs binarization processing on the linear low-signal image. The threshold used in the binarization processing is set based on the volume ratio to the intracranial volume. After the processing, the mask processing unit 17 uses the vascular mask image to remove vascular components from the AP-PADRE image.

[0063] (Steps S21 to S26) The vascular mask creation unit 14 removes point-like high signals with a width of one pixel from the bias field corrected vascular enhancement image. Thereafter, the vascular mask creation unit 14 performs processing on "high signals" instead of "low signals" in steps S11 to S16.

[0064] (Step S27) The vascular mask creation unit 14 creates a linear high-intensity image using the extracted 1-pixel-wide linear high-intensity extracted image, the 2-pixel-wide linear high-intensity extracted image, and the 3-pixel-wide linear high-intensity extracted image. The reason for processing the high-intensity components is that when creating a vascular mask image, arteries are also extracted, and high signals appear in arteries with flow. When making judgments based on the image, these components also affect the interpretation. For this reason, by processing steps S21 to S27, the vascular mask creation unit 14 removes the high-intensity components of the arterial components.

[0065] (Step S28) The vascular mask creating unit 14 performs binarization processing on the linear high signal image. After the processing, the mask processing unit 27 performs mask processing to extract information on the region of interest.

[0066] (Step S31) The vascular mask creating unit 14 performs dilation processing on the binarized linear low signal intensity image and linear high signal intensity image.

[0067] (Step S32) The vascular mask creating unit 14 creates and outputs a vascular mask image based on the dilated image.

[0068] 8, the vascular mask creation unit 14 may perform the process of step S10 (S11 to S16) and the process of step S20 (S21 to S26) simultaneously, in parallel, or in a time-division manner. Also, the vascular mask creation unit 14 may perform the process of steps S17 and S18 and the process of steps S27 and S28 simultaneously, in parallel, or in a time-division manner.

[0069] Note that, although the example shown in FIG. 8 shows an example in which the pixel widths are 1 to 3 and the removal process has three stages, this is not limiting. The pixel width and the number of stages of the removal process depend on the resolution of the original image, etc., and the removal process may be performed at any pixel width. In this case, the process of removing point-like low signals at an n-pixel width and removing / extracting linear low signals at an n-pixel width may be performed n times, and the process of removing point-like high signals at an n-pixel width and removing / extracting linear high signals at an n-pixel width may be performed n times.

[0070] (Processing method for eliminating dot-like signals) Next, we will explain the processing method for removing point-like signals. As a representative example, we will explain the processing procedure for removing point-like low signals with a width of one pixel. FIG. 9 is a flowchart of a processing procedure for removing point-like low signals in one pixel width according to this embodiment.

[0071] (Step S101) The vascular mask creating unit 14 acquires a bias field corrected vascular enhancement image (A).

[0072] (Step S102) The vascular mask creation unit 14 uses a 13-directional 3×3×3 pixel linear kernel (Fi1) for removing point-like low signals from the vascular enhancement image (A) to create a removed image (B) by performing morphological calculation processing according to the following equation (1) to remove point-like low signals of 1 pixel width. The 13-directional 3×3×3 pixel linear kernel will be described later.

[0073]

number

[0074] In addition, in formula (1), the operator "●" is a closing process.

[0075] (Step S103) The vascular mask creating unit 14 outputs the created removed image (B) in which the one-pixel-wide point low signal intensity has been removed.

[0076] The vascular mask creating unit 14 performs calculations on an image from which point low signals of two pixels in width have been removed, using Fi2 instead of Fi1 in equation (1) as a linear structural unit. The vascular mask creating unit 14 performs calculations on an image from which dot-like low signals of 3 pixels width have been removed, using Fi3 instead of Fi1 in equation (1) as a linear structural unit.

[0077] In addition, the vascular mask creation unit 14 calculates an image from which point high signals of one pixel width have been removed, and creates a removed image (B) from which linear low signals of one pixel width have been removed by morphological calculation processing using a 13-directional 3 x 3 x 3 pixel linear kernel (Fi1) for high signal removal, using the following equation (2).

[0078]

number

[0079] In addition, in the formula (2), the operator "o" indicates an opening process.

[0080] The vascular mask creating unit 14 performs calculations on an image from which point high signals of two pixels in width have been removed, using Fi2 instead of Fi1 in equation (2) as a linear structural unit. The vascular mask creating unit 14 performs calculations on an image from which point-like high signals of 3 pixels width have been removed, using Fi3 instead of Fi1 in equation (2) as a linear structural unit.

[0081] (Linear signal removal and extraction processing method) Next, we will explain the processing method for removing and extracting linear signals. As a representative example, we will explain the processing procedure for removing and extracting linear low signals with a width of one pixel. FIG. 10 is a flowchart of a processing procedure for removing linear low signal intensity in one pixel width according to this embodiment.

[0082] (Step S201) The vascular mask creating unit 14 acquires a removed image (B) in which point low signals of one pixel width have been removed.

[0083] (Step S202) The vascular mask creation unit 14 uses a 13-directional 3×3×3 pixel planar kernel (Gil) for removing linear low signals from the removed image (B) to create a removed image (C) by performing morphological calculation processing according to the following equation (3) to remove linear low signals of 1 pixel width. The 13-directional 3×3×3 pixel planar kernel will be described later.

[0084]

number

[0085] (Step S203) The vascular mask creating unit 14 outputs the created removed image (C) from which the linear low signal intensity of one pixel width has been removed.

[0086] (Step S204) The vascular mask creating unit 14 subtracts the removed image (C) from the removed image (B) to extract a linear low signal intensity of one pixel width, and outputs the extracted image (BC).

[0087] The vascular mask creating unit 14 performs calculations on the image from which linear low signals of 2 pixels width have been removed, using Gi2 instead of Gi1 in equation (3) as a planar structural unit. The vascular mask creating unit 14 performs calculations on an image from which linear low signals of 3 pixels wide have been removed, using Gi2 instead of Gi1 in equation (3) as a planar structural unit.

[0088] The vascular mask creation unit 14 also performs morphological calculations on the image from which linear high signals of one pixel width have been removed using a 13-directional 3 x 3 x 3 pixel planar kernel (Gi1) for high signal removal to create a removed image (C) from which planar low signals of one pixel width have been removed by the following equation (4): Note that the vascular mask creation unit 14 subtracts the removed image (B) from the removed image (C) to extract linear low signals of one pixel width, and outputs the extracted image (CB).

[0089]

number

[0090] Furthermore, the vascular mask creating unit 14 performs calculations on an image from which linear high signals of two pixels in width have been removed, using Gi2 instead of Gi1 in equation (4) as a planar structural unit. The vascular mask creating unit 14 performs calculations on an image from which linear high signals of 3 pixels width have been removed, using Gi3 instead of Gi1 in equation (4) as a planar structural unit.

[0091] (13-way 3x3x3 kernel) Next, we will explain the 13-direction 3x3x3 pixel kernel. Fig. 11 is a diagram showing 13-directional 3x3x3 pixel linear kernels. As shown in Fig. 11, each of the 13-directional linear kernels g201 to g213 is three adjacent linear pixels in a 3x3x3 pixel space. In this embodiment, point components are removed by using such linear kernels.

[0092] Fig. 12 is a diagram showing a 13-directional 3x3x3 pixel planar kernel. As shown in Fig. 12, each of the 13-directional planar kernels g301 to g313 is nine adjacent planar pixels in a 3x3x3 pixel space. In this embodiment, point components and line components can be classified by performing processing using linear structure units and planar structure units in the order shown in Figs. 8 to 10.

[0093] In the above example, a local low signal intensity component is removed from an AP-PADRE image by using a vascular mask image to remove vascular structures, and then a diffuse component is extracted. However, the present invention is not limited to this. For example, the image processing device 1 may remove a local low signal intensity component from an AP-PADRE image and then extract a diffuse component.

[0094] <Verification> Below, examples of verification results of visual evaluation using images created by the above-described embodiment and visual evaluation using images created by conventional technology will be described.

[0095] (First Verification) First, MRI images were processed using PADRE, and the vascular structures were removed using a vascular mask image. The images were visually evaluated by a physician, and the results of comparing the PiB-PET (Positron Emission Tomography) positive and negative groups are explained with reference to Figures 13 and 14.

[0096] FIG. 13 shows PADRE images (MRI-PADRE images) from a PiB-negative group (Normal Control) in a comparative example, from which the vascular structure and extra-brain components have been removed, and an example image used for comparison with PiB-PET. FIG. 14 shows PADRE images (MRI-PADRE images) from a PiB-positive group (Alzheimer's disease (AD)) in a comparative example, from which the vascular structure and extra-brain components have been removed, and an example image used for comparison with PiB-PET. Images g401 and g411 are MRI-PADRE images from which the vascular structure and extra-brain components have been removed using the method described above. Images g402 and g412 are PiB-PET images.

[0097] In a comparative study, 11 MRI-PADRE images of PiB-PET-negative and -positive cases were visually evaluated by physicians. Results showed that when vascular areas and extracerebral components were removed from the PADRE images, evaluation was easier than before vascular areas were removed. However, visual evaluation of whether the PADRE images were negative or positive was sometimes difficult using only the images from which vascular areas had been removed.

[0098] (Second verification) Next, we will explain the results of ROI (Region of Interest) analysis of cortical signals using the Automated Anatomical Labeling (AAL) atlas, with reference to Figures 15 to 18. In this verification, PADRE signals in each brain region were compared between the PiB-PET positive and negative groups. Figure 15 shows an example of an AAL atlas image set on standard brain coordinates, and an example of an image in which an ROI from the AAL atlas is overlaid on a PADRE image. Image g451 is an example of an image from the AAL atlas. Image g452 is an example of an image in which an AAL atlas image set on a standard brain has been converted to personal brain coordinates and then processed with a vascular mask and gray matter mask. Figure 16 shows that signals are distributed in the cortical region.

[0099] Reference 1; N. Tzourio-Mazoyer, B. Landeau, al, ``Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain'', NeuroImage, Volume 15, Issue 1, January 2002, Pages 273-289

[0100] Figures 16 to 18 show the average values ​​of PADRE signals normalized to the cerebellar average value for the PiB-positive and -negative groups in the cortex analyzed using ROI analysis with the AAL atlas. In each of graphs g511 to g522 in Figures 16 to 18, the left item g501 shows the results for the negative group, and the right item g502 shows the results for the positive group. In each of graphs g511 to g522 in Figures 16 to 18, the vertical axis shows the relative signal value when the average value for the cerebellar gray matter is set to 1.

[0101] Graph g511 shows the results for the inferior frontal lobe orbital region (right) (Frontal Inf Orb R). Graph g512 shows the results for the Rolandic area operculum (left) (Rolandic Oper L). Graph g513 shows the results for the Rolandic area operculum (right) (Rolandic Oper L). Graph g514 shows the results for the cuneus (left) (Cunrus L).

[0102] Graph g515 shows the results for the right superior occipital gyrus (Occipital Sup R). Graph g516 shows the results for the left middle occipital gyrus (Occipital Mid L). Graph g517 shows the results for the right middle occipital gyrus (Occipital Mid R). Graph g518 shows the results for the superior parietal lobule (left) (Parietal Sup L).

[0103] Graph g519 shows the results for the superior parietal lobule (right) (Parietal Sup R). Graph g520 shows the results for the supramarginal gyrus (right) (SupraMarginal R). Graph g521 shows the results for the right angular gyrus (Angular R). Graph g522 shows the results for the paracentral lobule (left) (Paracentral Lob L).

[0104] In addition, in each of graphs g511 to g522 in Figures 16 to 18, "*", "**", and "***" are evaluations based on P values, and the more asterisks there are, the more statistically significant the difference between the positive and negative groups is.

[0105] Figure 19 shows three-dimensional visualization of regions on the brain surface where there was a statistically significant difference between the mean values ​​of the PiB-positive and -negative cortical PADRE signals obtained by ROI analysis using the AAL atlas. Note that in Figure 19, the darker the region, the more pronounced the difference between the groups. Image g561 is an external image of the left hemisphere, and image g562 is an internal cross-sectional image of the left hemisphere. Image g571 is an external image of the right hemisphere, and image g562 is an internal cross-sectional image of the right hemisphere. In images g561, g562, g571, and g572, the darkest region has a p<0.001 value, for example, for region g551, and a p<0.01 value, for example, for region g552.

[0106] 16 to 18, the PADRE signal tends to be lower in the positive group than in the negative group. Also, as shown in Figure 19, the PiB-positive group has low signals near the parietal-occipital lobe.

[0107] Figure 20 shows color-coded histograms of the PiB-positive and -negative PADRE signal voxel values ​​in the local cortex of the brain, analyzed using the AAL atlas. Graph g591 shows the distribution of the negative and positive groups in the right superior occipital gyrus, graph g592 shows the distribution of the negative and positive groups in the right medial occipital gyrus, and graph g593 shows the distribution of the negative and positive groups in the superior parietal lobule (left). In graphs g591 to g593, distribution g581 shows the distribution of the positive group, and distribution g582 shows the distribution of the negative group. In graphs g591 to g593, the horizontal axis represents the normalized PADRE signal value, and the vertical axis represents the number of pixels. In graphs g591 to g593, each distribution line represents data for one subject. As shown in Figure 20, the positive group has a lower signal value distribution than the negative group.

[0108] 20 suggests that either the punctate component or the diffuse component (background component) in the cortex may reflect amyloid β. For this reason, in this embodiment, the punctate component and the diffuse component in the cortex are separated.

[0109] (Separation of punctate and diffuse components within the cortex) Next, a procedure for separating the local low signal component and the diffuse component will be described. FIG. 21 is a flowchart of the process of separating a local low signal component and a diffuse component according to this embodiment.

[0110] (Step S301) The separating unit 18 acquires the AP-PADRE image (D) after the mask processing by the mask processing unit 17.

[0111] (Step S302-p) (p is an integer from 1 to n) Separation unit 18 performs closing processing using the following equation (5) through morphological calculation processing using a spherical kernel (Hp) with a radius of p pixels. Note that separation unit 18 may perform the processing of steps S302-1 to S302-n in parallel or in a time-division manner.

[0112]

number

[0113] (Step S303-1) The separating unit 18 separates the local low signal of size 1 by calculating the local low signal of size 1 using morphological calculation processing according to the following equation (6).

[0114]

number

[0115] (Step S303-n) The separating unit 18 separates the local low signal of size n by calculating the local low signal of size n by morphological calculation processing using the following equation (7).

[0116]

number

[0117] (Step S304) The separating unit 18 outputs the n images S1, . . . , Sn calculated in steps S302-1 to S302-n as a diffuse component image.

[0118] (Third Verification) Next, the results of comparing and verifying the PADRE signals of the diffuse components in each ROI region between the PiB-PET positive group and the PiB-PET negative group will be described with reference to FIGS. 22 to 27.

[0119] Figures 22 to 25 show the average values ​​of normalized PADRE signal values ​​within the ROI of the diffuse component image for the PiB-positive group and the PiB-negative group. In each of graphs g611 to g624 in Figures 22 to 25, the left item g601 is the result for the negative group, and the right item g602 is the result for the positive group. In each of graphs g611 to g624 in Figures 22 to 25, the vertical axis represents the normalized signal value of the PADRE image of the diffuse component. Each of graphs g611 to g624 in Figures 22 to 25 shows the ROI analysis results of an image of only the diffuse component, with the patchy component (low signal component) of each area removed.

[0120] Graph g611 shows the results for the superior frontal lobe orbital region (right) (Frontal Sup Orb R). Graph g612 shows the results for the inferior frontal lobe orbital region (right) (Frontal Inf Orb R). Graph g613 shows the results for the Rolandic area operculum (left) (Rolandic Oper L). Graph g614 shows the results for the Rolandic area operculum (right) (Rolandic Oper L).

[0121] Graph g615 shows the results for the left cuneus (Cunrus L). Graph g616 shows the results for the right superior occipital gyrus (Occipital Sup R). Graph g617 shows the results for the left middle occipital gyrus (Occipital Mid L). Graph g618 shows the results for the right middle occipital gyrus (Occipital Mid R).

[0122] Graph g619 shows the results for the superior parietal lobule (left) (Parietal Sup L). Graph g620 shows the results for the superior parietal lobule (right) (Parietal Sup L). Graph g621 shows the results for the supramarginal gyrus (right) (SupraMarginal R). Graph g622 shows the results for the left angular gyrus (Angular L).

[0123] Graph g623 shows the results for the paracentral lobule (left) (Paracentral Lob L). Graph g624 shows the results for the inferior temporal gyrus (right) (Temporal Inf L).

[0124] 22 to 25 show a clearer difference between the negative and positive groups compared with FIGS. 16 to 18. This indicates that the diffuse component tends to reflect amyloid beta.

[0125] (Verification 4) Based on the results of Verification 3, the vascular image portion was masked from the PADRE image and patchy low-signal components were removed, after which only the diffuse PADRE signal was extracted and projected onto the brain surface for display, and an interpretation experiment was conducted. Figure 26 shows example images in which the average values ​​of the diffuse components extracted from PADRE images in the PiB-negative and -positive groups are projected onto the brain surface. In Figure 26, image g701 is an image in which the average values ​​of the negative group are projected, and image g702 is an image in which the average values ​​of the positive group are projected. Region g711 is an area with a high signal (i.e., low amyloid β). Region g712 is an area with a relatively low PADRE signal. From Figure 26, it can be visually seen that the signal in the parietal-occipital lobe is high in the negative group, and that the signal in the parietal-occipital lobe is lower in the positive group than in the negative group.

[0126] (Evaluation for each case) Next, examples in which the method of this embodiment is applied to each case will be described with reference to FIGS. FIG. 27 shows an image in which blood vessels and low signals are removed from an MRI image of a PiB-negative individual with spinocerebellar degeneration (SCD) using the method of this embodiment, and the extracted diffuse components are mapped onto the brain surface. The images were read by three doctors (a radiologist, a neurosurgeon, and an internist), and all three concluded that amyloid beta deposits were negative.

[0127] FIG. 28 shows an image in which blood vessels and low signals are removed from an MRI image of a healthy (NC) PiB-negative individual by the method of this embodiment, and the extracted diffuse components are mapped onto the brain surface. Three doctors read the images and all three concluded that amyloid beta deposits were negative.

[0128] Figure 29 shows an image of an MRI image of a PiB-positive individual with mild cognitive impairment (MCI due to AD), in which blood vessels and low signals were removed using the method of this embodiment, and the extracted diffuse components were mapped onto the brain surface. Three doctors read the images and all three judged them to be positive for amyloid beta deposits.

[0129] Figure 30 shows an MRI image of a PiB-positive individual with mild cognitive impairment (MCI due to AD) in which blood vessels and low signal intensity were removed using the method of this embodiment, and the extracted diffuse components were mapped onto the brain surface. The images were read by three doctors, with three positive results.

[0130] FIG. 31 is a diagram showing the interpretation results for 11 subjects, including those shown in FIGS. In Figure 31, the top table g801 shows the interpretation judgment results. In table g801, "-" indicates a result judged as negative, and "+" indicates a result judged as positive. Note that "positive" refers to a judgment that "amyloid beta is deposited." In FIG. 31, the bottom table g802 shows the results of evaluating the judgment results of each doctor in table g801. As shown in FIG. 31, when a doctor interprets the image obtained by the method of this embodiment, it is possible to predict amyloid β deposition with high accuracy.

[0131] Here, the AP-PADRE used in this embodiment will be further explained. A two-component model can be used, which assumes that iron present in the cerebral cortex is composed of two components: age-related iron, which accumulates with aging, and amyloid iron, which accumulates with amyloid accumulation. In this embodiment, AP-PADRE uses this two-component model to visualize amyloid-β-bound iron with emphasis based on the difference in phase difference.

[0132] Regarding age-related iron, research has been conducted to evaluate and predict the total amount of iron associated with amyloid beta (amyloid iron) and iron accumulated with age (age-related iron) based on a quantitative value called QSM obtained from phase component information of MRI images, without distinguishing between them. Thus, the prior art did not distinguish between amyloid iron and age-related iron. In contrast, the present embodiment distinguishes between the two and detects information based on amyloid iron.

[0133] In the above formulas (1) and (3), examples using max have been described, but sup may be used instead of max. Also, in the above formulas (2) and (4), examples using mix have been described, but inf may be used instead of mix.

[0134] The verification results will be further explained. As an example of extracting components other than diffuse components from an AP-PADRE image, a point component image will be described. In this case, the point component image is extracted from an AP-PADRE image obtained by removing vascular structures using a vascular mask image.

[0135] FIG. 32 is a diagram showing a point component image and a diffuse component image created from an AP-PADRE image of a patient with MCI (Mild Cognitive Impairment) due to Alzheimer's disease. The point component image g811 has strong contrast and can be easily recognized from the original AP-PADRE image. On the other hand, the diffuse component image g812 has a large signal difference, but its contrast is low, making it difficult to recognize in the original AP-PADRE image.

[0136] Figure 33 shows the calculated signal difference between the mean values ​​of the cerebellum-normalized AP-PADRE punctate component images for each ROI between the PiB-positive and PiB-negative groups, and the resulting color map projected onto the brain surface. Figure 34 shows the calculated signal difference between the mean values ​​of the cerebellum-normalized AP-PADRE diffuse component images for each ROI between the PiB-positive and PiB-negative groups, and the resulting color map projected onto the brain surface. Note that Figures 33 and 34 show grayscale representations of color images. Images g831, g832, g841, and g842 in Figures 33 and 34 are side views. Images g833, g834, g843, and g844 are cross-sectional views. The darker area g845 indicates that the PiB(-) value in the negative group is higher than the PiB(+) value in the positive group, indicating a larger signal difference.

[0137] As shown in Figure 33, there is little difference between the groups in the punctate component images. In contrast, as shown in Figure 34, in the diffuse component images, the PiB-positive group tends to have lower signal values ​​overall compared to the PiB-negative group. In each embodiment, verification, etc., the terms focal hypointensity, punctate, and patchy are used interchangeably.

[0138] (Fifth Verification) In the fifth validation study, quantitative analysis of AP-PADRE images of 191 patients who visited the Department of Aging and Geriatrics at Tohoku University Hospital with a primary complaint of forgetfulness was performed using the AAL atlas. Patients were divided into three groups based on comprehensive diagnoses obtained from their symptoms and course, medical history and comorbidities, psychological tests, blood tests, brain MRI, and cerebral blood flow SPECT (single photon emission computed tomography). The first group, the non-AD group, included cognitively normal individuals and patients with dementia due to causes other than Alzheimer's disease. The second group consisted of patients with mild cognitive impairment (MCI) due to Alzheimer's disease. The third group consisted of patients with Alzheimer's disease dementia (AD).

[0139] In this study, AP-PADRE images were subjected to morphological analysis to create diffuse component images, which were then normalized by dividing the signal by the signal from the cerebellar cortex or hippocampus. Furthermore, in this study, the average signal value of the image in 90 gray matter regions within the AAL atlas was calculated, and a one-tailed t-test was performed between the two groups to verify whether there was a difference in the mean values ​​between the groups. Figures 35 to 37 show the results of the non-AD group versus the MCI + Alzheimer's dementia group in the fifth study. Figures 38 to 40 show the results of the one-tailed t-test between the non-AD group and the Alzheimer's dementia group in the fifth study. Figures 41 to 43 show the results of the one-tailed t-test of the same ROI analysis results for the 11 cases with PiB data in the fifth study, divided into two groups: the PiB-negative group and the PiB-positive group. In Figures 35 to 43, the values ​​shown are p-values, with p<0.05 as the statistical significance level. The AD group is a group with Alzheimer's disease, the Non-AD group is a group with non-Alzheimer's disease, and the MCI group is a group with mild cognitive impairment.

[0140] In addition, in Figures 35 to 43, the values ​​on the left are values ​​normalized for the cerebellum, and the values ​​on the right are values ​​normalized for the hippocampus. Furthermore, Table g851 in Figure 35, Table g861 in Figure 38, and Table g871 in Figure 41 are results for the region Precentral L to Rolandic Oper L. Table g852 in Figure 35, Table g862 in Figure 38, and Table g872 in Figure 41 are results for the region Rolandic Oper R to Hippocampus L. Table g853 in Figure 36, Table g863 in Figure 39, and Table g873 in Figure 42 are results for the region Hippocampus R to Postcentral L. Table g854 in Figure 36, Table g864 in Figure 39, and Table g874 in Figure 42 are results for the region Postcentral R to Thalamus L. Table g855 in FIG. 37, table g865 in FIG. 40, and table g875 in FIG. 43 are the results for the region Thalamus R to Temporal R.

[0141] 35 to 37, when comparing the non-AD group and the MCI+AD group, normalization to the cerebellum revealed signal reduction in 21 of 90 cerebral regions in the MCI+AD group. When normalization to the hippocampus revealed signal reduction in 69 of 90 regions in the AD group. Next, as shown in Figures 38 to 40, when comparing the non-AD group and the AD group, signal reduction was observed in the AD group at 23 out of 90 locations in the cerebellum normalized images and at 79 out of 90 locations in the hippocampus normalized images.

[0142] As shown in Figures 41 to 43, in the normalized images of the cerebellum, signal reduction was observed in 14 of 90 areas in the PiB-positive group. On the other hand, in the normalized images of the hippocampus, signal reduction was observed in 3 of 90 areas in the PiB-positive group.

[0143] In clinical case studies, the hippocampal signal in AP-PADRE diffuse component images is highly correlated with age and MMSE, and signal elevations primarily reflecting structural atrophy of the hippocampus are expected. In Alzheimer's disease, pathological atrophy occurs selectively in the area surrounding the hippocampus. Studies based on clinical diagnosis included many patients with advanced AD who clearly had pathological atrophy of the hippocampus. Therefore, normalization using hippocampal signal is thought to improve the ability to distinguish Alzheimer's disease due to the synergistic effect of the amyloid-related signal reduction observed in AP-PADRE and the signal elevation due to hippocampal atrophy specific to Alzheimer's disease. On the other hand, when examining the relationship with PiB in a small number of cases, including MCI and early Alzheimer's dementia patients with little hippocampal atrophy, non-Alzheimer's dementia patients with hippocampal atrophy, and healthy individuals, we found that the amyloid-specific signal decrease in AP-PADRE was offset by the inclusion of hippocampal atrophy information through normalization of the hippocampal signal, resulting in a decrease in discrimination ability.

[0144] In the above-described embodiment, an AP-PADRE image created by amyloid-β-bound iron-phase contrast-enhanced imaging, which emphasizes amyloid-β-bound iron, was described as an example of a phase contrast-enhanced image created from an MRI image, but phase contrast-enhanced images are not limited to this.

[0145] As described above, in this embodiment, morphological calculation processing is performed on the AP-PADRE image to mask the vascular structure and separate local low-signal components (punctate components) from diffuse components, thereby creating an image that allows visual evaluation of the presence or absence of amyloid beta.

[0146] Current methods for assessing the deposition of amyloid beta in the brain involve cerebrospinal fluid testing and amyloid PET, which are invasive, expensive, and involve radiation exposure. In contrast, according to this embodiment, image processing is performed when capturing MRI images, eliminating the radiation exposure caused by PET and providing an inexpensive examination. For example, this embodiment can also be applied to examinations such as brain checkups, and has the potential to efficiently detect pre-symptomatic Alzheimer's disease early on, representing a major breakthrough in dementia treatment.

[0147] Furthermore, in the above example, image interpretation was performed on Alzheimer's dementia, but this is not limited to this. Images processed by the method of this embodiment could also be used to distinguish non-Alzheimer's dementia (corticobasal degeneration: CBDS) and other conditions. In clinical dementia settings, effective treatments vary depending on whether the condition is Alzheimer's disease or another pathology, and incorrect treatments can even have adverse effects. If the cause of dementia in a patient can be estimated using images from this embodiment, this would be extremely useful in determining a dementia treatment strategy.

[0148] Although the above explanation uses the term "amyloid β," in each embodiment, each example, each verification example, etc., "amyloid β" is also used to mean "amyloid plaque."

[0149] The image processing device 1 of this embodiment may be provided in an MRI imaging device, etc. The image processing device 1 may also be, for example, a personal computer, etc.

[0150] Note that a program for implementing all or part of the functions of the image processing device 1 of the present invention may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be loaded into a computer system and executed to perform all or part of the processing of the image processing device 1. Note that the term "computer system" as used herein includes hardware such as an OS and peripheral devices. The term "computer system" also includes a WWW system equipped with a homepage provision environment (or display environment). The term "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into computer systems. The term "computer-readable recording medium" also includes devices that retain a program for a certain period of time, such as volatile memory (RAM) within a computer system that acts as a server or client when the program is transmitted via a network such as the Internet or a communication line such as a telephone line.

[0151] The program may also be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by transmission waves in the transmission medium. Here, the "transmission medium" that transmits the program refers to a medium that has the function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may also be a program that realizes part of the above-mentioned functions. Furthermore, the program may be a so-called differential file (differential program) that can realize the above-mentioned functions in combination with a program already recorded in the computer system.

[0152] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0153] 1...image processing device, 11...image acquisition unit, 12...vascular enhancement image generation unit, 13...bias field correction unit, 14...vascular mask creation unit, 15...AP-PADRE image generation unit, 16...bias field correction unit, 17...mask processing unit, 18...separation unit, 19...brain surface projection image output unit, 20...3D-T1 weighted image generation unit, 21...alignment unit, 22...anatomical standardization unit, 23...coordinate conversion unit, 24...statistical image analysis result output unit, 25...template storage unit, 26...coordinate conversion unit, 27...mask processing unit, 28...region of interest analysis result output unit

Claims

1. The vascular mask production department a step of removing point-like low signals from an image obtained from magnetic resonance signals in which regions corresponding to blood vessels are enhanced in an MRI image or a blood vessel-enhanced image acquired by an image acquisition unit; removing linear hypointensities from the image from which the punctate hypointensities have been removed; extracting a linear hypointensity signal using the image from which the point hypointensity signal has been removed and the image from which the linear hypointensity signal has been removed; a step of removing point-like high signals from an image obtained from magnetic resonance signals in which regions corresponding to blood vessels are enhanced in the MRI image or from a blood vessel-enhanced image acquired by the image acquisition unit; removing linear hyperintensities from the image from which the point hyperintensities have been removed; extracting a linear hyperintensity signal using the image from which the point hyperintensity signals have been removed and the image from which the linear hyperintensity signals have been removed; creating a vascular mask image by performing a step in which a mask processing unit generates an image in which a vascular structure is removed from an image obtained from the magnetic resonance signals or a vascular-enhanced image acquired by the image acquisition unit using the vascular mask image; An image processing method comprising:

2. a separation unit separating a local low signal intensity component and a diffuse signal intensity component from the image from which the vascular structure has been removed; a step in which a brain surface projection image output unit projects the diffuse component onto the brain surface to generate a brain surface projection image; The image processing method of claim 1 further comprising:

3. the vascular mask creating unit creates the vascular mask image by morphological calculation processing; the separating unit separates the local low signal intensity image and the diffuse component image by the morphological operation processing. The image processing method according to claim 2 .

4. The vascular mask creating unit removing the point-like low signal intensity by morphological calculation using a 3×3×3 linear kernel in 13 directions; removing the linear low signal intensity from the image from which the point low signal intensity has been removed by morphological calculation using a 3×3×3 planar kernel in 13 directions; extracting the linear hypointensity signal using the image from which the point hypointensity signal has been removed and the image from which the linear hypointensity signal has been removed; removing the point-like high signals by morphological calculation using a 3×3×3 linear kernel in 13 directions; removing the linear high signal intensity from the image from which the point high signal intensity has been removed by morphological calculation using a 3×3×3 planar kernel in 13 directions; extracting the linear hyperintensities using the image from which the point hyperintensities have been removed and the image from which the linear hyperintensities have been removed; The image processing method according to any one of claims 1 to 3, comprising:

5. The vascular mask creating unit performing a process of removing the point-like low signals, a process of removing the linear low signals, a process of extracting the linear low signals, a process of removing the point-like high signals, a process of removing the linear high signals, and a process of extracting the linear high signals for different pixel widths; The image processing method according to claim 3 .

6. The separation unit is A closing process is performed on the image from which the vascular structure has been removed by morphological operation using a spherical kernel with a radius of p (p is an integer from 1 to n) pixels to separate the local low signal intensity of size n; A step of performing a closing process using a spherical kernel with a radius of n pixels on the image from which the vascular structure has been removed, and separating the diffuse image using p results; The image processing method according to claim 2 , comprising:

7. 7. The image processing method according to claim 1, further comprising a step in which the mask processing unit generates an image in which vascular structures are removed from a phase contrast weighted image created from the MRI image using the vascular mask image.

8. 8. The image processing method according to claim 7, wherein the phase-contrast weighted image is an AP-PADRE image created by amyloid-β-bound iron-phase-contrast weighted imaging of the MRI image.

9. The image processing method according to claim 1 , wherein the vascular mask creating unit extracts information about a region of interest using the vascular mask image.

10. The vascular mask creating unit 9. The image processing method according to claim 1, wherein information on the region of interest is extracted by performing mask processing based on the vascular mask image, a template of the region of interest, information obtained by aligning the acquired enhanced image with the image obtained from the magnetic resonance signals or the vascular enhanced image acquired by the image acquisition unit and aligning both images, and information obtained by converting standardized information into the individual brain coordinates based on information obtained by determining a transformation vector field between individual brain coordinates and standard brain coordinates by transforming the enhanced image by nonlinear transformation and aligning it with a template image of the enhanced image on standard brain coordinates.

11. On the computer, a step of removing point-like low signals from an image obtained from magnetic resonance signals in which regions corresponding to blood vessels are enhanced in an MRI image or a blood vessel-enhanced image acquired by an image acquisition unit; a step of removing linear hypointensities from the image from which the point hypointensities have been removed; extracting a linear hypointensity signal using the image from which the point hypointensity signal has been removed and the image from which the linear hypointensity signal has been removed; a step of removing point-like high signals from an image obtained from magnetic resonance signals in which regions corresponding to blood vessels are enhanced in the MRI image or from a blood vessel-enhanced image acquired by the image acquisition unit; a step of removing linear hyperintensities from the image from which the point hyperintensities have been removed; extracting a linear hyperintensity signal using the image from which the point hyperintensity signal has been removed and the image from which the linear hyperintensity signal has been removed, thereby creating a vascular mask image; generating an image from which a vascular structure has been removed using the vascular mask image from the image obtained from the magnetic resonance signals or the vascular enhancement image obtained by the image acquisition unit; program.

12. a vascular mask creating unit that creates a vascular mask image by performing the following steps: removing point-like hypointensities from an image obtained from magnetic resonance signals that have enhanced regions corresponding to blood vessels in an MRI image or from a vascular-enhanced image acquired by an image acquiring unit; removing linear hypointensities from the image from which the point-like hypointensities have been removed; extracting linear hypointensities using the image from which the point-like hypointensities have been removed and the image from which the linear hypointensities have been removed; removing point-like hyperintensities from an image obtained from magnetic resonance signals that have enhanced regions corresponding to blood vessels in an MRI image or from a vascular-enhanced image acquired by an image acquiring unit; removing linear hyperintensities from the image from which the point-like hyperintensities have been removed; and extracting linear hyperintensities using the image from which the point-like hyperintensities have been removed and the image from which the linear hyperintensities have been removed; a mask processing unit that generates an image by removing a vascular structure from the image obtained from the magnetic resonance signal or the vascular-enhanced image acquired by the image acquisition unit, using the vascular mask image; An image processing device comprising:

13. The image processing device according to claim 12 , wherein the vascular mask creating unit extracts information about a region of interest using the vascular mask image.

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