Image processing apparatus, method, and program

Abstract

The present invention provides an image processing device, method, and program that allows for easy adjustment of the contrast of diffusion-weighted images of multiple regions when combining them. [Solution] The processor saves correction conditions for each part to match the tonal range of the multiple first partial diffusion-weighted images, which are included in the first combined diffusion-weighted image obtained by combining multiple first partial diffusion-weighted images for each part obtained by photographing the subject in multiple parts. It then obtains multiple second partial diffusion-weighted images of the subject, corrects the tonal range of the multiple second partial diffusion-weighted images for each part using the saved correction conditions, and derives a second combined diffusion-weighted image by combining the corrected multiple second partial diffusion-weighted images.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to an image processing apparatus, method, and program. BACKGROUND ART

[0002] In recent years, the DWIBS (Diffusion Weighted Whole Body Imaging with Background Body Signal Suppression) method has been proposed as a method for evaluating the distribution of tumors throughout the human body. The DWIBS method focuses on the fact that the cell density of abnormal tissues such as malignant tumors and inflammation (hereinafter also referred to as "lesions") is higher than the cell density of normal tissues, and is a method for detecting lesions based on the level of movement of water molecules between cells. In the DWIBS method, a whole-body ADC (Apparent Diffusion Coefficient) value representing the diffusion characteristics of water molecules is calculated from a whole-body diffusion-weighted image acquired by imaging with an MRI (Magnetic Resonance Imaging) apparatus, and the state of tissues is evaluated using an ADC map obtained by imaging the ADC values in color. The diffusion-weighted image is an image that indicates the degree of microscopic irregular motion of water molecules (for example, Brownian motion) as a signal value.

[0003] The signal value of a diffusion-weighted image is lower as the degree of movement of water molecules is larger. In a diffusion-weighted image, the signal value indicating a lesion is higher than the signal value of normal tissue. However, a signal value comparable to the signal value indicating a lesion can also be obtained due to restriction of the irregular motion of water molecules, that is, diffusion restriction. For this reason, the DWIBS method uses an ADC map to distinguish whether a signal value is a signal value indicating a lesion or a signal value due to diffusion restriction. The ADC map is a color image obtained by classifying ADC values calculated for each pixel from diffusion-weighted images acquired by applying gradient magnetic fields of different intensities at different b-values in an MRI apparatus using threshold values. The b-value is a value indicating the intensity of the gradient magnetic field during imaging by the MRI apparatus.

[0004] Thus, the DWIBS method allows for the evaluation of the distribution of lesions throughout the body from an ADC map using ADC values ​​calculated from diffusion-weighted images. Compared to examinations using CT (Computed Tomography) devices, it has advantages such as no radiation exposure and no need for contrast agent administration.

[0005] In the DWIBS method used to date, multiple parts of the patient (stations), such as the head, chest, abdomen, and lower extremities, are first imaged by an MRI device. Diffusion-weighted images (DiffW-Weighted Images) are obtained from each of these parts, and these are then combined to generate a combined DiffW-Weighted Image representing the entire patient. An ADC value is then calculated from the combined DiffW-Weighted Image, and a superimposed image, which is an ADC map based on the calculated ADC value and the combined DiffW-Weighted Image, is displayed on a screen or similar device. Because the contrast of the DiffW-Weighted Images differs for each part, the window width (WW) and window level (WL) are adjusted to perform grayscale processing so that the contrast of the multiple DiffW-Weighted Images is the same, before the combined DiffW-Weighted Image is derived.

[0006] When combining multiple diffusion-weighted images in this way, methods have been proposed to smooth the boundaries between the images (see Patent Document 1).

[0007] On the other hand, the method described in Patent Document 1 smooths the boundaries of the images, but does not correct the contrast of each diffusion-weighted image. Therefore, it is conceivable to match the contrast of multiple diffusion-weighted images by setting window widths and window levels for each region, for example, by performing histogram analysis. [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] International Publication No. 2022 / 209298 [Overview of the Initiative] [Problems that the invention aims to solve]

[0009] However, adjusting the window width and window level for each body part is extremely time-consuming. In particular, DWIBS acquires diffusion-weighted images by applying different gradient magnetic fields using, for example, two different b-values. Therefore, if there are, for example, four body parts, it is necessary to adjust the window width and window level for eight series.

[0010] This disclosure is made in view of the above circumstances and aims to enable easy adjustment of the contrast of diffusion-weighted images of multiple regions when combining diffusion-weighted images of multiple regions. [Means for solving the problem]

[0011] The image display device according to this disclosure comprises a processor, The processor saves correction conditions for matching the tonal range of the multiple first partial diffusion-weighted images, which are included in the first combined diffusion-weighted image obtained by combining multiple first partial diffusion-weighted images acquired by imaging the subject in multiple parts, for each part. Obtain multiple second partial diffusion-weighted images of the subject, Using the saved correction conditions, the tonal range of multiple second partially diffuse-weighted images is corrected for each region. A second combined diffusion-weighted image is derived by combining multiple corrected second partial diffusion-weighted images.

[0012] In the image processing apparatus according to this disclosure, the processor analyzes each of the multiple first partially diffuse-weighted images to derive a correction condition which is the relative amount of change from the initial tonal condition to match the tonality of the multiple first partially diffuse-weighted images. The derived correction conditions may also be saved.

[0013] In the image processing apparatus according to this disclosure, the correction conditions may be a relative contrast value, which is the ratio of the range of corrected signal values ​​to the range of uncorrected signal values, and a relative change in brightness, which is the difference between the center value of the range of corrected signal values ​​relative to the range of uncorrected signal values ​​and the center value of the range of uncorrected signal values, for each of a plurality of first partially diffuse-weighted images.

[0014] In the image processing apparatus according to this disclosure, the processor may store correction conditions input by the operator.

[0015] In the image processing apparatus according to this disclosure, the processor displays a second coupled diffusion-weighted image. The system accepts instructions for adjusting the tonal range of multiple second partial diffusion-weighted images contained within the second combined diffusion-weighted image. The correction conditions may be updated based on the adjustment instructions.

[0016] The image processing method disclosed herein involves a computer saving correction conditions for matching the tonal range of multiple first partial diffusion-weighted images, which are included in a first combined diffusion-weighted image obtained by combining multiple first partial diffusion-weighted images acquired by photographing a subject in multiple parts, for each part. Obtain multiple second partial diffusion-weighted images of the subject, Using the saved correction conditions, the tonal range of multiple second partially diffuse-weighted images is corrected for each region. A second combined diffusion-weighted image is derived by combining multiple corrected second partial diffusion-weighted images.

[0017] The image processing program disclosed herein includes a procedure for saving correction conditions for matching the tonal range of multiple first partially diffuse-weighted images, which are included in a first combined diffuse-weighted image obtained by combining multiple first partially diffuse-weighted images acquired by photographing a subject in multiple parts, for each part. A procedure for obtaining multiple second partial diffusion-weighted images of a subject, a procedure of correcting the gradation of each of the plurality of second partial diffusion-weighted images for each site using the stored correction conditions; and causing a computer to execute a procedure of deriving a second combined diffusion-weighted image by combining the plurality of corrected second partial diffusion-weighted images.

[0018] Note that the technology of the present disclosure may also be applied to a program product. [Effects of the Invention]

[0019] According to the present disclosure, when combining diffusion-weighted images of a plurality of sites, the contrast of the diffusion-weighted images of the plurality of sites can be easily adjusted. [Brief Description of the Drawings]

[0020] [Figure 1] Figure showing a schematic configuration of an image processing system to which an image processing apparatus according to an embodiment of the present disclosure is applied [Figure 2] Figure showing a hardware configuration of the image processing apparatus according to the present embodiment [Figure 3] Functional configuration diagram of the image processing apparatus according to the present embodiment [Figure 4] Figure schematically showing the initial gradation processing [Figure 5] Figure for explaining histogram analysis [Figure 6] Figure for explaining processing when new imaging is performed for follow-up observation of the same patient [Figure 7] Flowchart showing processing performed in the present embodiment [Modes for Carrying Out the Invention]

[0021] Embodiments of this disclosure will be described below with reference to the drawings. First, the configuration of an information processing system to which the image processing device according to this embodiment is applied will be described. Figure 1 is a diagram showing the schematic configuration of the information processing system. In the information processing system shown in Figure 1, an image processing device 1 containing the image processing device according to this embodiment, a shooting device 2, and an information storage device 3 are connected in a manner that enables communication via a wired or wireless network 4. Network 4 is, for example, a LAN (Local Area Network) and a WAN (Wide Area Network).

[0022] Image processing device 1 is a computer on which the image processing program of this embodiment is installed. The computer may be a workstation or personal computer directly operated by the physician performing the diagnosis, or it may be a server computer connected to them via a network. The image processing program is stored in a storage device of the server computer connected to the network, or in network storage, in a state that is accessible from the outside, and is downloaded and installed on the computer used by the physician upon request. Alternatively, it may be recorded on a recording medium such as a DVD (Digital Versatile Disc) or CD-ROM (Compact Disc Read Only Memory), distributed, and installed on the computer from that recording medium.

[0023] The imaging device 2 acquires medical images of the patient by imaging the patient, who is the subject of the study. In this embodiment, the imaging device 2 is an MRI device, and the MRI device acquires diffusion-weighted images of the patient's entire body. At this time, diffusion-weighted images (hereinafter referred to as partial diffusion-weighted images) are acquired for each of the four parts of the patient: the head, chest, abdomen, and lower limbs, and the four partial diffusion-weighted images are combined to acquire a combined diffusion-weighted image of the patient's entire body. In this embodiment, in order to derive the ADC map, partial diffusion-weighted images are acquired using two types of gradient magnetic fields: a low b value (e.g., b value = 0) and a high b value (e.g., b value = 1000).

[0024] The information storage device 3 is a computer that stores and manages medical images, as well as medical documents such as image interpretation reports and medical records created by physicians regarding the medical images. It includes a storage device for storing medical images and medical documents. In this embodiment, the medical images are partially diffuse-weighted images and combined diffuse-weighted images. The information storage device 3 also stores correction conditions derived as described later.

[0025] Next, an image processing apparatus according to this embodiment will be described. Figure 2 is a diagram showing the hardware configuration of the image processing apparatus according to this embodiment. As shown in Figure 2, the image processing apparatus 1 includes a CPU (Central Processing Unit) 11, a memory 16 as a temporary storage area, and a non-volatile storage unit 13. The image processing apparatus 1 also includes a display 14 such as a liquid crystal display or an organic EL (Electro-Luminescence) display, and an input device 15 such as a keyboard. The display 14 and the input device 15 may be integrated as a touch panel display. The image processing apparatus 1 also includes a network I / F (Interface) 17 connected to a network 4. The CPU 11, memory 16, storage unit 13, and network I / F 17 are connected to a bus 19.

[0026] The storage unit 13 is implemented by an HDD (Hard Disk Drive), SSD (Solid State Drive), or flash memory, etc. The image processing program 12 is stored in the storage unit 13 as a storage medium. The CPU 11 reads the image processing program 12 from the storage unit 13, expands it into memory 16, and executes the expanded image processing program 12.

[0027] The image processing program 12 is stored in a memory device of a server computer connected to the network, or in network storage, in a state that allows external access, and is downloaded and installed on the image processing device 1 upon request.

[0028] Next, the functional configuration of the image processing apparatus according to this embodiment will be described. Figure 3 is a diagram showing the functional configuration of the image processing apparatus according to this embodiment. As shown in Figure 3, the image processing apparatus 1 comprises an information acquisition unit 21, a grayscale processing unit 22, an output unit 23, and a display control unit 24. The CPU 11 executes the image processing program 12, and the CPU 11 functions as the information acquisition unit 21, the grayscale processing unit 22, the output unit 23, and the display control unit 24.

[0029] The information acquisition unit 21 acquires partial diffusion-weighted images for each body part from the imaging device 2 and / or the information storage device 3. The acquired partial diffusion-weighted images are two types of diffusion-weighted images with different b values. The information acquisition unit 21 also acquires correction conditions for the diffusion-weighted images from the information storage device 3.

[0030] The tone processing unit 22 performs tone processing to correct the tone of the partially diffuse-weighted image. To this end, the tone processing unit 22 performs tone processing to derive correction conditions for the partially diffuse-weighted image. Here, for the diagnosis of a patient, follow-up observation using diffusion-weighted images is performed to understand the state of the lesion over time. The correction conditions are derived at the time of the initial imaging for follow-up observation. Then, the correction conditions are derived based on the tone processing conditions for the partially diffuse-weighted image acquired at the time of the initial imaging.

[0031] Figure 4 is a schematic diagram showing the initial grayscale processing. In this embodiment, four partial diffusion-weighted images G1 to G4 of the patient's head, chest, abdomen, and lower limbs are assumed to have been acquired. Partial diffusion-weighted images G1 to G4 are examples of the first partial diffusion-weighted images of this disclosure. Note that the partial diffusion-weighted images G1 to G4 shown in Figure 4 all represent sagittal sections of the patient. The grayscale processing unit 22 performs grayscale processing on each of the partial diffusion-weighted images G1 to G4 so that they have the same grayscale. To this end, the grayscale processing unit 22 performs analysis on the partial diffusion-weighted images G1 to G4. In this embodiment, the analysis is histogram analysis, but is not limited to this. For histogram analysis, the grayscale processing unit 22 derives histograms H1 to H4 of the partial diffusion-weighted images G1 to G4. Because the partially diffuse-weighted images G1-G4 differ in the size of the structures and patient regions they contain, the histograms H1-H4 have different signal distributions.

[0032] Figure 5 is a diagram illustrating histogram analysis. Figure 5 shows the histogram H1 of the partially diffuse-weighted image G1. Histogram H1 includes the distribution of signal values ​​B1 including the patient and the distribution of signal values ​​B2 for the background not including the patient. The grayscale processing unit 22 determines the range of signal values ​​WWs1 and the center value WLs1 of the signal value range WWs1 in the distribution of signal values ​​B1 including the patient in histogram H1. The grayscale processing unit 22 then performs grayscale processing to match the signal value range WWs1 to the window width WWb0 which is the reference window width when displaying all partially diffuse-weighted images G1 to G4, and to match the center value WLs1 to the center value WLb0 of the window width WWb0 which is the reference window width when displaying all partially diffuse-weighted images G1 to G4. This derives the window width and window level for partially diffuse-weighted image G1.

[0033] The gradation processing unit 22 performs this gradation processing on all partially diffuse-weighted images G1 to G4, and derives processed partially diffuse-weighted images Gc1 to Gc4 that have undergone gradation processing.

[0034] The derivation unit 23 combines the partially diffuse-weighted images Gc1 to Gc4, which have undergone grayscale processing, to derive the processed combined diffuse-weighted image Gc0.

[0035] The display control unit 24 displays the processed coupled diffusion-weighted image Gc0 on the display 14.

[0036] The operator may further adjust the window width and window level of the four partial diffusion-weighted images Gc1 to Gc4 for the combined diffusion-weighted image Gc0 displayed on the display 14. If the operator further adjusts the gradation, the gradation processing unit 22 determines the final window widths WW1 to WW4 and window levels WL1 to WL4 for the four partial diffusion-weighted images Gc1 to Gc4. If the operator does not adjust the gradation, the gradation processing unit 22 determines the window widths WW1 to WW4 and window levels WL1 to WL4 as the final window widths WW1 to WW4 and window levels WL1 to WL4.

[0037] The gradation processing unit 22 derives the ratio of the final window widths WW1 to WW4 to the reference window width WWb0 as relative contrast values ​​C1 to C4 using equation (1) below. The gradation processing unit 22 also derives the relative brightness change amounts K1 to K4, which are the difference between the window levels WL1 to WL4 and the reference window level WLb0, using equation (2) below. Note that equations (1) and (2) show the contrast values ​​C1 and change amounts K1 for the partially diffuse-enhanced image G1, but the contrast values ​​C2 to C4 and change amounts K2 to K4 can be similarly determined for the partially diffuse-enhanced images G2 to G4. C1 = WW1 / WWb0 (1) K1 = (WL1 - WLb0) / WWb0 (2)

[0038] The gradation processing unit 22 transmits the derived contrast values ​​C1 to C4 and change amounts K1 to K4 to the information storage device 3 for storage as correction conditions. Alternatively, the correction conditions may be stored in the memory unit 13.

[0039] The information acquisition unit 21 acquires partially diffuse-weighted images with two different b values. Therefore, the correction conditions are derived and saved for each b value.

[0040] Next, we will explain how to handle cases where new imaging is performed for follow-up purposes for the same patient. Figure 6 is a diagram illustrating how to handle cases where new imaging is performed for follow-up purposes for the same patient.

[0041] The information acquisition unit 21 acquires the newly acquired partial diffusion-weighted images G11 to G14 and the correction conditions stored in the information storage device 3. If the correction conditions are stored in the storage unit 13, the unit acquires the correction conditions stored in the storage unit 13. In this case as well, two types of partial diffusion-weighted images with different b values ​​are acquired.

[0042] The gradation processing unit 22 derives the window widths WW11 to WW14 and window levels WL11 to WL14 for the partially diffuse-weighted images G11 to G14. For example, the window width WW11 and window level WL11 for the partially diffuse-weighted image G11 can be derived using the following equations (3) and (4). Similarly, the window widths WW12 to WW14 and window levels WL12 to WL14 for the partially diffuse-weighted images G12 to G14 can be derived using the contrast values ​​C2 to C4 and the change amounts K2 to K4, in the same manner as equations (3) and (4). The gradation processing unit 22 performs gradation processing on the partially diffuse-weighted images G11 to G14 using the window widths WW11 to WW14 and window levels WL11 to WL14, and derives the processed partially diffuse-weighted images Gc11 to Gc14. WW11 = WWb0 × C1 (3) WL11 = WLb0 × K1 (4)

[0043] The derivation unit 23 combines the partially diffuse-weighted images Gc11 to Gc14, which have undergone grayscale processing, to derive the processed combined diffuse-weighted image Gc10. The derivation of the processed combined diffuse-weighted image Gc10 is performed for each b value. The derivation unit 23 then derives an ADC map from the combined diffuse-weighted images Gc10 with different b values.

[0044] The display control unit 24 superimposes the ADC map onto the processed coupled diffusion-weighted image Gc10 with the larger b value, and displays the coupled diffusion-weighted image Gc10 with the ADC map superimposed on it on the display 14.

[0045] Next, the processing performed in this embodiment will be described. Figure 7 is a flowchart of the processing performed in this embodiment. It is assumed that the correction conditions derived during the initial inspection are stored in the storage unit 13. The information acquisition unit 21 acquires the partially diffuse-weighted images G11 to G14 and the correction conditions (step ST1). The gradation processing unit 22 derives window widths WW11 to WW14 and window levels WL11 to WL14 from the partially diffuse-weighted images G11 to G14 using the correction conditions, i.e., contrast values ​​C1 to C4 and change amounts K1 to K4, and performs gradation processing on the partially diffuse-weighted images G11 to G14 using the derived window widths WW11 to WW14 and window levels WL11 to WL14 (step ST2). The derivation unit 23 combines the partially diffuse-weighted images G11 to G14 that have undergone gradation processing to derive a combined diffuse-weighted image Gc10 (step ST3). The display control unit 24 displays the combined diffuse-weighted image Gc10 (step ST4) and terminates the processing.

[0046] Thus, in this embodiment, the gradation processing of the partially diffuse-weighted images G11 to G14 is performed using the saved correction conditions. Therefore, although the initial inspection is time-consuming, for subsequent inspections, the contrast of multiple partially diffuse-weighted images G11 to G14 can be easily adjusted by using the saved correction conditions.

[0047] In the above embodiment, the combined diffusion-weighted image Gc10, which has already undergone gradation processing, may be subjected to further gradation processing. In this case, the contrast value and change amount may be derived using the results of the further gradation processing, and the saved correction conditions may be updated using the derived contrast value and change amount.

[0048] Furthermore, in the above embodiment, the gradation processing unit 22 derives correction conditions through analysis during the initial gradation processing, but this is not the only way to do so. The partially diffuse-weighted images G1 to G4 may be displayed on the display 14, and the operator may directly adjust the window width and window level using the input device 15, determine the correction conditions using the results, and save them.

[0049] In this embodiment, each process is executed on any computer. Furthermore, any computer may execute these processes using a processor as hardware, a program as software, or a combination thereof. In that case, the processor is configured to work in cooperation with the program to execute the various processes in this embodiment, and can function as a unit or means in this embodiment. Also, the execution order of the processes by the processor is not limited to the order described and may be changed as appropriate. Any computer may be a general-purpose computer, a computer designed for a specific purpose, a workstation, or any other system capable of executing each process.

[0050] A processor may consist of one or more hardware components, and the type of hardware is not limited. For example, a processor may consist of a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a programmable logic device such as an FPGA (Field Programmable Gate Array), a dedicated circuit for executing a specific process such as an ASIC (Application Specific Integrated Circuit), a GPU (Graphic Processing Unit), or an NPU (Neural Processing Unit). Furthermore, the type of hardware may be a combination of different types of hardware. When multiple hardware components are configured to execute one or more processes of a processor, these components may reside in physically separate devices or in the same device. Also, in any embodiment, the order of each process performed by the processor is not limited to the order described above and may be changed as appropriate. Hardware is composed of electrical circuits (circuitry) that combine circuit elements such as semiconductor elements.

[0051] Furthermore, the program may be firmware or software such as microcode. Alternatively, the program may be, for example, a set of program modules, each function of which may be implemented by a processor configured to perform its respective function. The program may be program code or multiple code segments stored on one or more non-temporary computer-readable media (e.g., storage media or other storage). The program may be divided and stored on multiple non-temporary computer-readable media located in physically separate devices. Program code or code segments may represent any combination of procedures, functions, subprograms, routines, subroutines, modules, software packages, classes, or instructions, data structures, or program statements. Program code or code segments may be connected to other code segments or hardware circuits by sending and receiving information, data, arguments, parameters, or memory contents.

[0052] Furthermore, although the above embodiment describes an embodiment in which the image processing program 12 is pre-stored (installed) in the storage unit 13, the invention is not limited to this. The image processing program 12 may be provided in the form of a recording medium such as a CD-ROM (Compact Disc Read Only Memory), DVD-ROM (Digital Versatile Disc Read Only Memory), or USB (Universal Serial Bus) memory. Alternatively, the image processing program 12 may be provided in the form of a download from an external device via a network.

[0053] The technology disclosed herein extends to all program products. Program products include all forms of products for providing programs. For example, program products include programs provided via networks such as the Internet, and non-temporary computer-readable recording media such as CD-ROMs, DVDs, and USB memory sticks on which programs are stored.

[0054] The following are additional notes to this disclosure. (Additional note 1) Equipped with a processor, The aforementioned processor, Correction conditions for matching the tonal range of the multiple first partial diffusion-weighted images, which are included in the first combined diffusion-weighted image obtained by combining multiple first partial diffusion-weighted images acquired by photographing the subject in multiple parts, are saved for each part. Multiple second partial diffusion-weighted images of the subject are obtained, Using the saved correction conditions, the tonal range of the plurality of second partial diffusion-weighted images is corrected for each part. An image processing apparatus that derives a second combined diffusion-weighted image by combining the multiple corrected second partial diffusion-weighted images. (Additional note 2) The processor analyzes each of the plurality of first partial diffusion-weighted images to derive a relative change in tone from the initial tone condition for matching the tone of the plurality of first partial diffusion-weighted images as the correction condition. The image processing apparatus described in Appendix 1 for storing the derived correction conditions. (Additional note 3) The image processing apparatus according to Appendix 2, wherein the correction conditions are a relative contrast value which is the ratio of the range of corrected signal values ​​to the range of uncorrected signal values ​​for each of the plurality of first partially diffuse-weighted images, and a relative change in brightness which is the difference between the center value of the range of corrected signal values ​​relative to the range of uncorrected signal values ​​and the center value of the range of uncorrected signal values. (Additional note 4) The processor is an image processing apparatus according to Appendix 1 that stores the correction conditions input by the operator. (Additional note 5) The processor displays the second coupled diffusion-weighted image, The system receives instructions for adjusting the grayscale of the plurality of second partial diffusion-weighted images included in the second combined diffusion-weighted image. An image processing apparatus according to any one of the appendix items 1 to 4, which updates the correction conditions based on the adjustment instructions. (Additional note 6) The computer saves correction conditions for matching the tonal range of the multiple first partial diffusion-weighted images, which are included in a first combined diffusion-weighted image obtained by combining multiple first partial diffusion-weighted images acquired by photographing the subject in multiple parts, for each part. Multiple second partial diffusion-weighted images of the subject are obtained, Using the saved correction conditions, the tonal range of the plurality of second partial diffusion-weighted images is corrected for each part. An image processing method for deriving a second combined diffusion-weighted image by combining a plurality of corrected second partial diffusion-weighted images. (Additional note 7) A procedure for saving correction conditions for matching the tonal range of multiple first partial diffusion-weighted images, which are included in a first combined diffusion-weighted image obtained by combining multiple first partial diffusion-weighted images acquired by photographing the subject in multiple parts, for each part, A procedure for obtaining multiple second partially diffuse-weighted images of the subject, A procedure for correcting the tonal range of the multiple second partial diffusion-weighted images for each part using the saved correction conditions, An image processing program that causes a computer to perform a procedure for deriving a second combined diffusion-weighted image by combining a plurality of corrected second partial diffusion-weighted images. [Explanation of Symbols]

[0055] 1 Image processing device 2. Imaging device 3 Information storage device 4 Network 11 CPU 12 Image Processing Programs 13 Storage section 14 displays 15 Input Devices 16 memory 17 Network Interface 19 bus 21 Information Acquisition Department 22-level grayscale processing unit 23 Derivation part 24 Display Control Unit B1 Distribution of signal values ​​including patients B2 Distribution of signal values ​​in the background excluding patients G1-G4, G11-G14 Partially Diffusion-Weighted Images H1-H4 histogram Gc1-Gc4, Gc11-Gc14 processed partially diffuse-weighted images Gc0 and Gc10 processed coupled diffusion-weighted images WLs1: Center value of the signal value range WWs1 signal value range

Claims

1. Equipped with a processor, The aforementioned processor, Correction conditions for matching the tonal range of the multiple first partial diffusion-weighted images, which are included in the first combined diffusion-weighted image obtained by combining multiple first partial diffusion-weighted images for each part obtained by photographing the subject in multiple parts, are saved for each part. Multiple second partial diffusion-weighted images of the subject are obtained, Using the saved correction conditions, the tonal range of the plurality of second partial diffusion-weighted images is corrected for each part. An image processing apparatus that derives a second combined diffusion-weighted image by combining a plurality of corrected second partial diffusion-weighted images.

2. The processor analyzes each of the plurality of first partial diffusion-weighted images to derive a relative change from the initial tone condition for matching the tone of the plurality of first partial diffusion-weighted images as the correction condition. The image processing apparatus according to claim 1, which stores the derived correction conditions.

3. The image processing apparatus according to claim 2, wherein the correction conditions are a relative contrast value which is the ratio of the range of corrected signal values ​​to the range of uncorrected signal values ​​for each of the plurality of first partially diffuse-weighted images, and a relative change in brightness which is the difference between the center value of the range of corrected signal values ​​relative to the range of uncorrected signal values ​​and the center value of the range of uncorrected signal values.

4. The image processing apparatus according to claim 1, wherein the processor stores the correction conditions input by the operator.

5. The processor displays the second coupled-diffusion-weighted image, The system receives instructions for adjusting the grayscale of the plurality of second partial diffusion-weighted images included in the second combined diffusion-weighted image. The image processing apparatus according to any one of claims 1 to 4, which updates the correction conditions based on the adjustment instructions.

6. The computer saves correction conditions for matching the tonal range of the multiple first partial diffusion-weighted images, which are included in a first combined diffusion-weighted image obtained by combining multiple first partial diffusion-weighted images acquired by photographing the subject in multiple parts, for each part. Multiple second partial diffusion-weighted images of the subject are obtained, Using the saved correction conditions, the tonal range of the plurality of second partial diffusion-weighted images is corrected for each part. An image processing method for deriving a second combined diffusion-weighted image by combining a plurality of corrected second partial diffusion-weighted images.

7. A procedure for saving correction conditions for matching the tonal range of multiple first partial diffusion-weighted images, which are included in a first combined diffusion-weighted image obtained by combining multiple first partial diffusion-weighted images acquired by photographing the subject in multiple parts, for each part, A procedure for obtaining multiple second partially diffuse-weighted images of the subject, A procedure for correcting the tonal range of the plurality of second partially diffuse-weighted images for each part using the saved correction conditions, An image processing program that causes a computer to perform a procedure for deriving a second combined diffusion-weighted image by combining a plurality of corrected second partial diffusion-weighted images.

Images