Medical image processing method and device and medical imaging equipment

Abstract

The embodiment of the invention provides a medical image processing method and device and medical imaging equipment. The medical image processing method comprises the following steps: acquiring a computed tomography image of a scanned object; generating radiotherapy plan parameters, wherein the radiotherapy plan parameters are generated based on the acquired computed tomography image and a pre-established incidence relation between pixel values of all pixels of the computed tomography image and the radiotherapy plan parameters used for making a radiotherapy plan; and generating a radiotherapy plan parameter abnormity prompt, wherein the abnormity prompt is generated when the local radiotherapy plan parameters of the scanned object are abnormal. According to the invention, the abnormity of the radiotherapy plan parameter based on the CT image can be automatically identified; moreover, when it is recognized that the radiotherapy plan parameters are abnormal, the user is prompted, so that the burden of the user is relieved, and the operation efficiency and the accuracy of the radiotherapy plan are improved.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of medical devices, and in particular, to a medical image processing method, apparatus, and medical imaging device. Background Art

[0002] Medical imaging devices are used to scan patients in a non-invasive manner to obtain medical images of anatomical tissues of interest to the patients, so as to assist doctors in diagnosis. By way of example, CT (Computed Tomography), that is, computed tomography, uses detectors to collect data of X-rays after passing through the object to be detected, and then processes these collected X-ray data to obtain projection data. These projection data can be used to reconstruct CT images. CT images can be used for auxiliary diagnosis or treatment. For example, CT images are used for dose planning in a radiotherapy treatment planning system (TPS).

[0003] It should be noted that the above introduction to the technical background is only for the convenience of clearly and completely explaining the technical solutions of the present application and facilitating the understanding of those skilled in the art. Summary of the Invention

[0004] Abnormal radiotherapy treatment plan parameters The inventors found that in the existing TPS dose planning method based on CT images, during operation, users (such as doctors, imaging technicians, etc.) need to manually check in CT images or TPS dose planning (in image form or table form) whether there are abnormal dose planning parameters and in which areas of the radiotherapy treatment plan parameters are abnormal. For example, abnormal dose planning parameters include missing dose planning parameters. The existing operation efficiency is low and the accuracy varies depending on the professional level of the user. Moreover, if there are local abnormal dose planning parameters in the generated TPS dose planning, abnormal radiotherapy treatment plan parameters require CT image reconstruction and TPS dose planning to be performed again, which will take a lot of time, resulting in a further reduction in operation efficiency and even bringing a bad experience to users and patients.

[0005] In view of at least one of the above technical problems, embodiments of the present application provide a medical image processing method, apparatus, and medical imaging device.

[0006] According to a first aspect of the embodiments of the present application, the present application provides a medical image processing method, the method comprising:

[0007] Obtaining a computed tomography image of a scanned object;

[0008] Generate radiotherapy plan parameters, which are generated based on the acquired computed tomography (CT) images and the pre - established association relationship between the pixel values of each pixel in the CT images and the radiotherapy plan parameters used for formulating radiotherapy plans;

[0009] Generate an abnormal prompt for radiotherapy plan parameters, which is generated when there are abnormalities in the radiotherapy plan parameters of a local part of the scanned object.

[0010] According to the second aspect provided by the embodiments of the present application, the present application provides a medical image processing device, which includes:

[0011] An acquisition unit, which is used to acquire the computed tomography (CT) images of the scanned object;

[0012] A generation unit, which is used to generate radiotherapy plan parameters, which are generated based on the acquired computed tomography (CT) images and the pre - established association relationship between the pixel values of each pixel in the CT images and the radiotherapy plan parameters used for formulating radiotherapy plans;

[0013] A prompt unit, which is used to generate an abnormal prompt for radiotherapy plan parameters, which is generated when there are abnormalities in the radiotherapy plan parameters of a local part of the scanned object.

[0014] According to the third aspect provided by the embodiments of the present application, the present application provides a medical imaging device, which includes a memory and a processor. The memory stores a computer program, and the processor is configured to execute the computer program to implement the medical image processing method as described in the embodiments of the first aspect.

[0015] One of the beneficial effects of the embodiments of the present application is that: through the medical image processing method, device and equipment of the present application, for the CT images reconstructed based on the scan data or the images used for radiotherapy plans, the automatic identification of abnormalities in radiotherapy plan parameters is carried out, which reduces the burden on users, improves the identification efficiency and the accuracy of radiotherapy plans;

[0016] Moreover, when an abnormality in the radiotherapy plan parameters is identified, an abnormal prompt is automatically generated to give a warning to the user, indicating that there are abnormalities in the radiotherapy plan parameters. Thus, the user can perform medical image reconstruction based on this abnormal prompt, which improves the operation efficiency and the accuracy of radiotherapy plans.

[0017] Specific embodiments of the present application are disclosed in detail with reference to the following description and the accompanying drawings, indicating the ways in which the principles of the embodiments of the present application can be adopted. It should be understood that the embodiments of the present application are not limited in scope thereby. Within the spirit and terms of the appended claims, the embodiments of the present application include many changes, modifications, and equivalents. Description of the Drawings

[0018] The accompanying drawings included are used to provide a further understanding of the embodiments of the present application, which form a part of the specification, illustrate the embodiments of the present application, and, together with the written description, explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can obtain other embodiments based on these drawings without creative efforts. In the drawings:

[0019] Figure 1 is a schematic diagram of a CT device according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of a CT imaging system according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a medical image processing method according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of the steps of generating radiotherapy plan parameters according to an embodiment of the present application;

[0023] Figures 5A to 5C is a schematic diagram of a second medical image according to an embodiment of the present application;

[0024] Figure 6 is a schematic diagram of an anomaly prompt according to an embodiment of the present application;

[0025] Figure 7 is a schematic diagram of a second medical image highlighting abnormal data according to an embodiment of the present application;

[0026] Figure 8 is a schematic diagram of a medical image processing device according to an embodiment of the present application;

[0027] Figure 9 is a schematic diagram of a medical imaging device according to an embodiment of the present application. Detailed Embodiments

[0028] Referring to the accompanying drawings, the foregoing and other features of the embodiments of the present application will become apparent through the following description. In the description and drawings, specific embodiments of the present application are specifically disclosed, which show some embodiments in which the principles of the embodiments of the present application can be adopted. It should be understood that the present application is not limited to the described embodiments. On the contrary, the embodiments of the present application include all modifications, variations, and equivalents falling within the scope of the appended claims.

[0029] In the embodiments of the present application, terms such as "first" and "second" are used to distinguish different elements in terms of name, but do not indicate the spatial arrangement or time sequence of these elements, and these elements should not be limited by these terms. The term "and / or" includes any one and all combinations of one or more of the associated listed terms. Terms such as "comprising", "including", and "having" mean the presence of the stated features, elements, components, or assemblies, but do not exclude the presence or addition of one or more other features, elements, components, or assemblies.

[0030] In the embodiments of the present application, the singular forms "a", "the", etc. include the plural forms and should be broadly understood as "a kind" or "a class" rather than being limited to the meaning of "one"; in addition, the term "the" should be understood to include both the singular form and the plural form unless the context clearly indicates otherwise. In addition, the term "according to" should be understood as "at least partially according to...", and the term "based on" should be understood as "at least partially based on...", unless the context clearly indicates otherwise.

[0031] Features described and / or illustrated for one embodiment can be used in the same or similar manner in one or more other embodiments, combined with the features in other embodiments, or replace the features in other embodiments. The term "comprising / including" as used herein means the presence of features, whole things, steps, or components, but does not exclude the presence or addition of one or more other features, whole things, steps, or components.

[0032] The device for obtaining medical image data (or also referred to as medical imaging or medical image data) described in this article can be applicable to various medical imaging modalities, including but not limited to computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, positron emission tomography (PET) devices, single photon emission computed tomography (SPECT) devices, PET / CT, PET / MR, or any other suitable medical imaging device. In this application, computed tomography images include pseudo-CT images converted from medical images of modalities such as PET, SPECT, MRI, and others.

[0033] For example, a CT device continuously scans a cross-section of a part of a scanned object with X-rays. The X-rays passing through this layer are received by a detector, converted into visible light, or directly converted after receiving photon signals, and then undergo a series of processes for image reconstruction; an MRI device is based on the principle of nuclear magnetic resonance. By emitting radiofrequency pulses to the scanned object and receiving the electromagnetic signals released by the scanned object, an image is formed through reconstruction.

[0034] The system for obtaining medical image data can include the aforementioned medical imaging device, can also include a separate computer device connected to the medical imaging device, and can further include a computer device connected to the Internet cloud. This computer device is connected to the medical imaging device through the Internet or to a memory storing medical images. The imaging method can be independently or jointly implemented by the aforementioned medical imaging device, the computer device connected to the medical imaging device, and the computer device connected to the Internet cloud. For example, the system for obtaining medical image data can be a CT imaging system, etc.

[0035] Exemplarily, the embodiments of this application are described below in conjunction with an X-ray computed tomography (CT) device. Those skilled in the art will understand that the embodiments of this application can also be applicable to other medical imaging devices.

[0036] Figure 1 is a schematic diagram of the CT device in the embodiments of this application, schematically showing the situation of the CT device 100. As Figure 1As shown, the CT device 100 includes a scanning gantry 101 and a patient table 102; the scanning gantry 101 has an X-ray source 103, and the X-ray source 103 projects an X-ray beam towards a detector assembly or a collimator 104 on the opposite side of the scanning gantry 101. The object to be detected 105 can lie flat on the patient table 102 and move into the scanning gantry opening 106 along with the patient table 102; through the scanning of the X-ray source 103, medical image data of the object to be detected 105 can be obtained.

[0037] Figure 2 is a schematic diagram of a CT imaging system according to an embodiment of the present application, schematically showing a block diagram of the CT imaging system 200. As Figure 2 shown, the detector assembly 104 includes a plurality of detector units 104a and a data acquisition system (DAS, Data Acquisition System) 104b. The plurality of detector units 104a sense the projected X-rays passing through the object to be detected 105.

[0038] The DAS 104b converts the collected information into projection data for subsequent processing according to the sensing of the detector units 104a. During the scanning for acquiring X-ray projection data, the scanning gantry 101 and the components mounted thereon rotate around the rotation center 101c.

[0039] The rotation of the scanning gantry 101 and the operation of the X-ray source 103 are controlled by a control mechanism 203 of the CT imaging system 200. The control mechanism 203 includes an X-ray controller 203a that provides power and timing signals to the X-ray source 103, and a scanning gantry motor controller 203b that controls the rotation speed and position of the scanning gantry 101. The image reconstruction device 204 receives the projection data from the DAS 104b and performs image reconstruction. The reconstructed image is transmitted as an input to a computer 205, and the computer 205 stores the image in a mass storage device 206.

[0040] The computer 205 also receives commands and scanning parameters from an operator through a console 207. The console 207 has some form of operator interface, such as a keyboard, a mouse, a voice-activated controller, or any other suitable input device. The associated display 208 allows the operator to observe the reconstructed image and other data from the computer 205. The commands and parameters provided by the operator are used by the computer 205 to provide control signals and information to the DAS 104b, the X-ray controller 203a, and the scanning gantry motor controller 203b. Additionally, the computer 205 operates a patient table motor controller 209 to control the patient table 102 to position the object to be detected 105 and the scanning gantry 101. In particular, the patient table 102 moves the object to be detected 105 entirely or partially through Figure 1 the scanning gantry opening 106.

[0041] The above has schematically described the devices and systems for acquiring medical image data in the embodiments of the present application, but the present application is not limited thereto. The medical imaging device can be a CT device, a PET-CT, or any other suitable imaging device. The storage device can be located inside the medical imaging device, inside a server external to the medical imaging device, inside an independent medical image storage system (such as, PACS, Picture Archiving and Communication System), and / or inside a remote cloud storage system.

[0042] In addition, the medical imaging workstation can be set locally to the medical imaging device, that is, the medical imaging workstation is set adjacent to the medical imaging device. The medical imaging workstation and the medical imaging device can be jointly located in a scanning room, a radiology department, or the same hospital. The medical image cloud platform analysis system can be located away from the medical imaging device, for example, set at a cloud end communicating with the medical imaging device.

[0043] As an example, after the medical imaging device in a medical institution completes an imaging scan, the scanned data is stored in the storage device; the medical imaging workstation can directly read the scanned data and perform image processing through its processor. As another example, the medical image cloud platform analysis system can read the medical images in the storage device through remote communication to provide "Software as a Service" (SaaS, Software as a Service). SaaS can exist between hospitals, between a hospital and an imaging center, or between a hospital and a third-party online diagnosis and treatment service provider.

[0044] In the embodiments of the present application, the medical image can include images obtained through various scanning methods. For example, for a Computed Tomography (CT) device, its scanning methods mainly include three types: positioning scan, axial scan, and helical scan. Among them, during the positioning scan, the X-ray tube and the detector are stationary, and the scanning bed moves, so as to obtain a large-range positioning image; during the axial scan, the scanning bed needs to be stepped to the position to be imaged, the scanning bed stops moving, the tube and the detector rotate 360° (or more than 360°) to collect data, the tube and the detector stop working, and the scanning of the current layer is completed. The scanning bed is stepped to the next position again, and the foregoing scanning process is repeated to complete the scanning of the next layer, and so on; during the helical scan, while the tube and the detector are rotating, the scanning bed also moves at a uniform speed, so as to collect continuous data.

[0045] The process of medical image scanning is schematically described above. The embodiments of the present application will be specifically described below with reference to the accompanying drawings. In the following embodiments, a CT device is taken as an example of the medical imaging device, and the description content is equally applicable to other medical imaging devices.

[0046] An embodiment of the present application provides a medical image processing method. Figure 3 It is a schematic diagram of the medical image processing method according to an embodiment of the present application. As Figure 3 shown, the medical image processing method includes the following operations:

[0047] Step 301, obtaining a computed tomography image of a scanned object;

[0048] Step 302, generating radiotherapy plan parameters, where the radiotherapy plan parameters are generated based on the obtained computed tomography image and the association relationship established in advance between the pixel values of each pixel of the computed tomography image and the radiotherapy plan parameters for formulating a radiotherapy plan;

[0049] Step 303, generating an abnormal prompt for radiotherapy plan parameters, where the abnormal prompt is generated when there is an abnormality in the radiotherapy plan parameters of a local part of the scanned object.

[0050] The embodiments of the present application can automatically identify abnormalities in radiotherapy plan parameters in a radiotherapy plan based on computed tomography images (CT images), reduce the burden on users, improve the identification efficiency and the accuracy of the radiotherapy plan; and, when an abnormality in the radiotherapy plan parameters is identified, an abnormal prompt is automatically generated to give a warning to the user, indicating that there is an abnormality in the radiotherapy plan parameters. Thus, the user can perform medical image reconstruction based on the abnormal prompt, improving the operation efficiency and the accuracy of the radiotherapy plan.

[0051] In the embodiments of the present application, the "object to be scanned" may include any person to be scanned and imaged, or a part of a person's tissue, or an object, or a part of an object area.

[0052] In the embodiments of the present application, the operation of scanning the object to be scanned can be referred to the Figure 1 schematic diagram shown. As Figure 1 shown, the object to be scanned 105 is placed on the patient table 102 and moved into the scanning gantry opening 106 along with the patient table 102. The X-ray source 103 projects an X-ray beam towards the detector assembly or collimator 104 on the opposite side of the scanning gantry 101 for scanning, so as to obtain scanned volume data.

[0053] In step 301 of the present application, a computed tomography image of the scanned object, i.e., a CT image, is obtained. Herein, the CT image refers to an image obtained by reconstructing the original data scanned by a medical imaging device or a medical imaging system. The CT image includes a plurality of volume pixels, and each volume pixel corresponds to a pixel value, which is a CT value, also known as a CT number, or can be expressed as CT#.

[0054] In step 302 of the present application, radiotherapy plan parameters are generated. Among them, the radiotherapy plan parameters are generated based on the obtained computed tomography image and the pre-established correlation between the pixel values of each pixel of the computed tomography image and the radiotherapy plan parameters for formulating the radiotherapy plan.

[0055] In some embodiments, the radiotherapy plan parameters refer to the parameters for calculating the dose in a Treatment Planning System (TPS). The radiotherapy plan parameters include, but are not limited to, at least one of Relative Electron Density (RED), Stopping Power Ratio (SPR), and Mass Density (MD).

[0056] In some embodiments, the correlation between the pixel values of each pixel of the computed tomography image and the radiotherapy plan parameters for formulating the radiotherapy plan is pre-established and stored in a Look Up Table (LUT). The look-up table is pre-established and stored. The specific method of establishing the look-up table can refer to relevant prior art. For example, a specified phantom, such as a Gammex phantom, is scanned. The specified positions of the phantom have inserts, and the materials and positions of the inserts can be set according to regulations. The inserts are made of known materials, that is, parameters such as the relative electron density and stopping power ratio of the inserts are known parameters. After scanning, the CT value of each insert is calculated, and then a one-to-one correspondence between the CT value and the radiotherapy plan parameters corresponding to the insert is established and stored to obtain the look-up table.

[0057] In some embodiments, the look-up table is updated and maintained regularly or irregularly. For example, it is updated during annual inspections, after machine calibration, or after the machine replaces hardware, etc.

[0058] In some embodiments, different look-up tables are respectively established for different radiotherapy planning parameters. Therefore, when the generated radiotherapy planning parameters are different, the corresponding look-up tables need to be obtained. For example, when relative electron density needs to be generated, the obtained look-up table is the RED LUT, which includes the correlation between CT values and relative electron density; when stopping power ratio needs to be generated, the obtained look-up table is the SPR LUT, which includes the correlation between CT values and stopping power ratio.

[0059] In some embodiments, different look-up tables are respectively established for different scanning information. Therefore, when the scanning information of the object to be scanned is different, the obtained look-up tables may be different. In some embodiments, the scanning information of the object to be scanned includes but is not limited to information such as the scanning part, age, gender, body type, scanning protocol, etc. of the object to be scanned. The scanning part is, for example, parts of the human body such as the head, neck, chest and abdomen, pelvis, and limb joints. The scanning protocol is, for example, a specific scanning protocol formulated according to factors such as the scanning part, age, gender, body type (such as children, adults), and scanning part of the patient. This scanning protocol sets specific parameters such as the scanning current mA and the pre-collimator. For example, when the scanning part of the object to be scanned is different, the obtained look-up tables may be different. For example, the look-up table obtained when the scanning part is the head is different from the look-up table obtained when the scanning object is the chest and abdomen. For another example, when the information such as the age, gender, and body type of the object to be scanned is different, the obtained look-up tables may be different. For example, when the scanning part is the head, the look-up table obtained when the object to be scanned is a child is different from the look-up table obtained when the object to be scanned is an adult.

[0060] In some embodiments, the medical image processing method further includes: determining a look-up table according to the scanning information of the object to be scanned and the radiotherapy planning parameters to be generated; or, in response to a user's selection operation, obtaining a look-up table, where the user's selection operation is, for example, the user's selection operation of the look-up table, or the user's selection operation of the scanning information of the object to be scanned and the radiotherapy planning parameters to be generated, etc. Among them, the scanning information and the radiotherapy planning parameters to be generated are as described above. In addition, when the radiotherapy planning parameter to be generated is the stopping power ratio (SPR), the particle type (such as protons, heavy ions, etc.) and energy level (such as 100 MeV) also need to be obtained simultaneously to more accurately match the corresponding look-up table.

[0061] In some embodiments, the storage format of the look-up table includes a table format or an image format. Among them, in the table-form look-up table, the CT value and the value of the radiotherapy planning parameter are recorded one-to-one. In the image-form look-up table, the CT value is represented by the x-axis (or y-axis), and the value of the radiotherapy planning parameter is represented by the y-axis (or x-axis). Multiple discrete points are recorded in the two-dimensional coordinate system to represent the correlation between the CT value and the value of the radiotherapy planning parameter.

[0062] Figure 4 It is a schematic diagram of the steps for generating radiotherapy plan parameters in an embodiment of the present application. As Figure 4 shown, in step 302, generating radiotherapy plan parameters includes:

[0063] Step 401, obtaining the pixel value corresponding to each pixel in the computed tomography image, that is, the CT value;

[0064] Step 402, determining whether there is an abnormality in the radiotherapy plan parameters of the local part of the object to be scanned; if not, execute step 403;

[0065] Step 403, determining the radiotherapy plan parameters corresponding to the pixel value according to the correlation between the pixel value and the radiotherapy plan parameters.

[0066] In some embodiments, in step 401, the manner of obtaining the CT value of each pixel in the computed tomography image can refer to the prior art.

[0067] In some embodiments, in step 402, determining whether there is an abnormality in the radiotherapy plan parameters of the local part of the object to be scanned includes: determining whether the correlation between the pixel value of each pixel in the computed tomography image and the radiotherapy plan parameters exists in the pre-established correlation. For example, determining whether the correlation between the pixel value of each pixel in the computed tomography image and the radiotherapy plan parameters exists according to the correlation recorded in the determined look-up table, wherein the manner of determining the look-up table can refer to the foregoing embodiments of this article.

[0068] In some embodiments, determining whether the correlation between the pixel value of each pixel in the computed tomography image and the radiotherapy plan parameters exists in the pre-established correlation includes: determining whether the pixel value exists in the determined look-up table, or whether the pixel value is within the data range of the look-up table.

[0069] In some embodiments, in step 402, determining whether there is an abnormality in the radiotherapy plan parameters of the local part of the object to be scanned includes: when it is determined that there is no radiotherapy plan parameter corresponding to the pixel values of at least one pixel on each side of the pixel of the obtained computed tomography image in the pre-established correlation, determining that the radiotherapy plan parameter of the pixel is abnormal.

[0070] In some embodiments, for the determination of whether a pixel value or a CT value exists or is recorded in a look-up table, for example, Table 1 shows a look-up table SRP LUT, which shows the mapping relationship between the CT value and the radiotherapy plan parameter SPR when the particle type is proton and the energy level is 100 MeV.

[0071] Table 1 SPR LUT

[0072] CT value SPR -775 0.2 -490 0.5 -49 0.96 -29 0.99 55 1.06 59 1.07 225 1.16

[0073] Assume that a CT value corresponding to a computed tomography (CT) image is a, and the lookup table is the SPR LUT shown in Table 1. When there is a CT value in the SPR LUT that is the same as a, for example, when a = -490, it is determined that there is an association relationship corresponding to the CT value a in the lookup table SPR LUT, that is, the SPR LUT records the CT value of -490. At this time, through step 403 above, the radiotherapy plan parameter SPR corresponding to the CT value a = -490 in the SPR LUT is determined to be b = 0.5.

[0074] In some embodiments, for the judgment of whether the pixel value or CT value is within the data range of the lookup table. For example, referring to Table 1, assume that a CT value corresponding to a computed tomography (CT) image is c, and the lookup table is the SPR LUT shown in Table 1. In the SPR LUT, the data range corresponding to the CT value is [-775, 225]. When the CT value c is within the data range [-775, 225] of the SPR LUT and is not equal to the endpoints -775 or 225 of the data range, that is, -775 < b < 225, it is determined that there is a mapping relationship corresponding to the CT value c in the lookup table SPR LUT.

[0075] At this time, in step 403 above, a fitting curve is determined according to the association relationship between at least two CT values and SPR in the lookup table; and the radiotherapy plan parameter SPR corresponding to the CT value c is determined according to the fitting curve. Among the association relationships between the at least two CT values and SPR, some CT values are greater than the CT value c, and some CT values are less than the CT value c. For example, assume that a CT value of a computed tomography (CT) image is c1 = 57. According to the mapping relationships between two CT values and SPR in the lookup table, for example, according to the two mapping relationships (CT value, SPR) = (55, 1.06) and (CT value, SPR) = (59, 1.07), the first fitting curve is determined, and the SPR d1 = y1 corresponding to the CT value c1 = 57 is determined according to this first fitting curve. Another example, assume that a CT value of a computed tomography (CT) image is c2 = 30. According to the mapping relationships between multiple CT values and SPR in the lookup table, for example, according to (CT value, SPR) = (-49, 0.96), (CT value, SPR) = (-29, 0.99), (CT value, SPR) = (55, 1.06), and (CT value, SPR) = (59, 1.07), the second fitting curve is determined, and the SPR d2 = y2 corresponding to the CT value c2 = 30 is determined according to this second fitting curve.

[0076] In some embodiments, the more the association relationships between the CT values in the look-up table used for determining the fitting curve and the radiotherapy plan parameters are, and the closer the CT values in the association relationships are to the pixel values of the computed tomography image, the higher the accuracy of the radiotherapy plan parameters determined by the fitting curve.

[0077] Thus, through the above embodiments, based on the acquired computed tomography image and the pre-established association relationship between the pixel values of each pixel of the computed tomography image and the radiotherapy plan parameters for formulating the radiotherapy plan, the pixel values corresponding to each pixel in the computed tomography image are mapped to the radiotherapy plan parameters, thereby improving the accuracy of determining the abnormality of the radiotherapy plan parameters.

[0078] As described above, in this application, in step 402, the judgment on whether there is an abnormality in the radiotherapy plan parameters of the local part of the scanned object is implemented based on the pre-established association relationship between the CT values and the radiotherapy plan parameters. At the same time, in step 403, determining the radiotherapy plan parameters corresponding to the pixel values of the pixels of the computed tomography image is also implemented based on this association relationship. Although Figure 4 step 402 in

[0079] In some embodiments, step 302 of generating the radiotherapy plan parameters further includes: generating a radiotherapy plan parameter table or generating a radiotherapy plan parameter map. Thus, the generated radiotherapy plan parameters corresponding to the computed tomography image can be presented in the form of a table or an image, meeting the different viewing needs of users.

[0080] In some embodiments, the radiotherapy plan parameter map may be an image formed by replacing the CT values of each pixel in the computed tomography image with their corresponding radiotherapy plan parameter values. For example, when the radiotherapy plan parameter is RED, the radiotherapy plan parameter map is a RED image, when the radiotherapy plan parameter is SPR, the radiotherapy plan parameter map is an SPR image, and when the radiotherapy plan parameter is MD, the radiotherapy plan parameter map is an MD image.

[0081] In some embodiments, the computed tomography (CT) images and radiotherapy planning parameter maps include tomographic images of different planes of the same part of the object to be scanned. For example, when the object to be scanned is a human abdomen, the CT images and radiotherapy planning parameter maps can be tomographic images of any plane such as the coronal, sagittal, and axial planes of the abdomen. Figures 5A to 5C is a schematic diagram of the CT image or radiotherapy planning parameter map according to an embodiment of the present application, where Figures 5A to 5C are medical images obtained by scanning the coronal, sagittal, and axial planes of the human abdomen, respectively.

[0082] In some embodiments, after generating the radiotherapy planning parameters in step 302, the medical image processing method further includes (not shown): determining the display position of the radiotherapy planning parameters corresponding to the pixel values of the pixels in the CT image according to the positions of the pixels in the CT image, and displaying them to obtain the radiotherapy planning parameter map. Among them, the position of the pixel in the CT image is consistent with the display position of the radiotherapy planning parameter associated with the pixel value of the pixel in the radiotherapy planning parameter map. The display form of the radiotherapy planning parameters is related to their actual values and display rules, and specific references can be made to related technologies.

[0083] In some embodiments, the radiotherapy planning parameter map can be displayed independently of the CT image, or the radiotherapy planning parameter map can also be superimposed and displayed with the CT image.

[0084] In some embodiments, please continue to refer to Figure 4 , when it is determined in step 402 that there is an abnormality in the radiotherapy planning parameters of a local part of the object to be scanned, that is, when it is determined that the association relationship between the pixel values of the pixels in the CT image and the radiotherapy planning parameters does not exist in the pre-established association relationship, the medical image processing method further includes: step 404, determining that there is an abnormality in the radiotherapy planning parameters.

[0085] In this way, in the pre-established association relationship, or in the determined lookup table, there may not be an association relationship between the pixel values of the CT image and the radiotherapy planning parameters. At this time, through step 404, the pixel values of the pixels in the CT image whose association relationship with the radiotherapy planning parameters does not exist in the pre-established association relationship or the determined lookup table, that is, the pixel values not existing in the lookup table or the pixel values that cannot obtain the radiotherapy planning parameters through "interpolation" matching, are identified as abnormal data.

[0086] For the pixel values determined to be abnormal data, it is also impossible to determine the corresponding radiotherapy plan parameters through the pre-established association relationship. Correspondingly, in the generated radiotherapy plan parameter map, the radiotherapy plan parameters corresponding to the pixel values identified as abnormal data are also abnormal data. That is, in the generated radiotherapy plan parameter map, at the position corresponding to the position of the pixel corresponding to the pixel value determined to be abnormal data in the computed tomography image, effective display cannot be performed, but is displayed in the form of abnormal radiotherapy plan parameters.

[0087] In this way, according to the recognition result of whether there is abnormal data in the pixel value of the pixel of the computed tomography image, it is possible to determine whether there is abnormal radiotherapy plan parameters in the radiotherapy plan parameter map. Thus, automatic recognition of abnormal radiotherapy plan parameters is achieved.

[0088] For example, please continue to refer to Table 1. Assume that the pixel value (or CT value) of a pixel in the computed tomography image is e, and its value is e = 230, and the lookup table is the SPR LUT shown in Table 1. It is determined according to step 402 that there is no association relationship between the CT value e and the SPR in the SPR LUT. At this time, through the above step 404, the CT value e is determined to be abnormal data, and further it is determined that there is abnormal radiotherapy plan parameters in the generated radiotherapy plan parameter map.

[0089] In step 303, a radiotherapy plan parameter abnormality prompt is generated, and the abnormality prompt is generated when there are abnormal radiotherapy plan parameters in the local area of the scanned object. That is, when there are abnormal radiotherapy plan parameters in the local area of the scanned object, a radiotherapy plan parameter abnormality prompt is generated.

[0090] In some embodiments, the generating the radiotherapy plan parameter abnormality prompt includes: generating an abnormality prompt in at least one of the obtained computed tomography image, the radiotherapy plan parameter table, the radiotherapy plan parameter map, or the superimposed map of the obtained computed tomography image and the radiotherapy plan parameter map.

[0091] In some embodiments, the abnormality prompt includes at least one of the ways of highlighted display, thickened contour, color coding, and prompt box for prompting.

[0092] In this way, by means of emphasized display for abnormality prompt, it is convenient for users to quickly master the existence of abnormal data, enhance the prompt or warning effect, and improve the inspection efficiency and the accuracy of the radiotherapy plan.

[0093] In some embodiments, when the abnormality prompt is displayed using a prompt box, it is in the form of a dialog box or a pop-up window, and the abnormality is prompted through text or pictures. For example, Figure 6It is a schematic diagram of an abnormal prompt according to an embodiment of the present application. As Figure 6 shown, the text "There is an abnormality in the radiotherapy plan parameters in the medical image" can be displayed in the dialog box 600. In addition, content related to the position, size, etc. of the abnormality in the radiotherapy plan parameters can be further displayed.

[0094] In some embodiments, generating an abnormal prompt for radiotherapy plan parameters includes: determining a target area corresponding to abnormal data in a medical image; and displaying the target area in at least one of highlighting, thickening the outline display, color-coding display, and text display. Wherein, the medical image includes at least one of the computer tomography image, radiotherapy plan parameter table, radiotherapy plan parameter map, or the superimposed map of the computer tomography image and the radiotherapy plan parameter map described above. For example, in a computer tomography image, the abnormal data includes an abnormal CT value, that is, a CT value for which there is no associated relationship with the radiotherapy plan parameters in the pre-established association relationship. Another example is that in a radiotherapy plan parameter map, abnormal radiotherapy plan parameters, that is, non-existent or abnormally displayed radiotherapy plan parameters. In this way, it is convenient for users to intuitively grasp the position and quantity of abnormal data and improve the inspection efficiency.

[0095] In some embodiments, the method for determining the target area includes determining the target area according to the position of the pixel corresponding to the abnormal data in the medical image. Wherein, in the radiotherapy plan parameter map, the target area is the area where the abnormality in the radiotherapy plan parameters is located. Thus, it can accurately reflect the position of the abnormality in the radiotherapy plan parameters relative to the object to be scanned.

[0096] In some embodiments, for example, when thickening the outline display of the target area, generate and thicken the boundary outline of the target area. Further, the prompt or warning effect can be enhanced by changing the color of the bounding box. Thus, it is convenient for users to understand that there is abnormal data and quickly and accurately determine the position and / or range of the abnormal data.

[0097] In some embodiments, for example, when highlighting or color-coding the display of the target area, fill the target area with a preset color and display it. Further, the filled color can be set by the user himself. Thus, the prompt or warning effect can be enhanced, which is convenient for users to understand that there is abnormal data, quickly and accurately determine the position and / or range of the abnormal data, and know the degree of data abnormality.

[0098] In some embodiments, for text display of the target area, for example, a dialog box pops up at any position around the target area, and information related to the abnormal data corresponding to the target area, such as the CT value corresponding to the target area, is displayed in the dialog box.

[0099] Figure 7 It is a schematic diagram for highlighting abnormal data in an embodiment of the present application. Figure 7 The target area 701 highlighted by thickening the contour, the target area 702 highlighted in black, and the target area 703 highlighted by a tooltip and thickening the contour are shown. In the way of highlighting the target area 703 by a tooltip and thickening the contour, the tooltip can be set to be displayed when the mouse hovers, that is, a dialog box pops up when the mouse cursor moves to the target area 703, so as to avoid affecting the user's viewing of the medical image. In addition, other highlighting methods can also be used, and the present application does not limit this.

[0100] In some embodiments, the medical image processing method further includes: in response to a user's access operation, displaying images of each plane included in the medical image and abnormal data. Wherein, the medical image includes images of the object to be scanned in multiple planes. For example, the second medical image includes images of the object to be scanned in the coronal, sagittal, and axial planes, as shown in Figures 5A to 5C For example. The medical image includes at least one of the computer tomography image, the radiotherapy plan parameter table, the radiotherapy plan parameter map, or the superimposed map of the computer tomography image and the radiotherapy plan parameter map described above.

[0101] The user's access operation generates a trigger signal. In response to the trigger signal generated by the access operation, the images of each plane to be displayed are determined, and for each plane image, the abnormal data is displayed in the image in the manner of highlighting the abnormal data in the foregoing embodiments. Thus, the abnormal data can be stereoscopically displayed in multiple planes, facilitating the user to view.

[0102] In some embodiments, the medical image processing method further includes: in response to a user's operation of updating the lookup table, updating the lookup table, and regenerating radiotherapy plan parameters based on the updated lookup table and the computer tomography image. The user's operation of updating the lookup table generates a trigger signal. In response to the trigger signal generated by the operation of updating the lookup table, the lookup table is updated, and the update includes updating the data in the same lookup table or replacing the lookup table with a new lookup table. Subsequently, using the medical image processing method of the present application, the computer tomography image is processed based on the updated lookup table. Thus, it is beneficial to eliminate the abnormal radiotherapy plan parameters that may occur due to lookup table errors and improve the quality of the generated radiotherapy plan parameters.

[0103] In some embodiments, the medical image processing method further includes: in response to a user's operation of removing metal artifacts, regenerating radiotherapy plan parameters based on the computer tomography image.

[0104] According to the foregoing image processing method, in the case where an abnormal prompt exists in the generated radiotherapy plan parameters, since the abnormality may be caused by metal artifacts in the computed tomography image, the user can activate the function of removing metal artifacts to calibrate the pixel values (or CT values) of the pixels in the computed tomography image. Among them, the method of calibrating CT values using the metal artifact function can refer to the prior art. Subsequently, using the medical image processing method of the present application, processing is performed based on the calibrated pixel values of the computed tomography image and the look-up table. Thereby, it is beneficial to eliminate the abnormality of the radiotherapy plan parameters that may occur due to the presence of metal foreign objects in the examined object and improve the quality of the generated radiotherapy plan parameters.

[0105] Through the above embodiments, the present application automatically identifies the abnormality of radiotherapy plan parameters for the CT image reconstructed based on the scan data or the image for radiotherapy planning, reducing the burden on the user and improving the identification efficiency and the accuracy of the radiotherapy plan. Moreover, when an abnormality of the radiotherapy plan parameters is identified, an abnormal prompt is automatically generated to give a warning to the user, indicating that there is an abnormality in the radiotherapy plan parameters. Thus, the user can perform medical image reconstruction based on this abnormal prompt, improving the operation efficiency and the accuracy of the radiotherapy plan.

[0106] The embodiment of the present application also provides a medical image processing device, and the same content as the above embodiment will not be repeated.

[0107] Figure 8 is a schematic diagram of the medical image processing device according to the embodiment of the present application. As Figure 8 shown, the medical image processing device 800 according to the embodiment of the present application includes:

[0108] An acquisition unit 801, which is used to acquire the computed tomography scan image of the scanned object;

[0109] A generation unit 802, which is used to generate radiotherapy plan parameters, and the radiotherapy plan parameters are generated based on the acquired computed tomography scan image and the association relationship established in advance between the pixel values of each pixel of the computed tomography scan image and the radiotherapy plan parameters for formulating the radiotherapy plan;

[0110] A prompt unit 803, which is used to generate an abnormal prompt for the radiotherapy plan parameters, and the abnormal prompt is generated when there is an abnormality in the radiotherapy plan parameters of a local part of the scanned object.

[0111] In some embodiments, the generation unit 802 is specifically used to: generate a radiotherapy plan parameter table or generate a radiotherapy plan parameter graph.

[0112] In some embodiments, the radiotherapy plan parameters include at least one of Relative Electron Density (RED), Stopping Power Ratio (SPR), and Mass Density (MD).

[0113] In some embodiments, the association relationship includes the correspondence between the CT values of the pixels in the computed tomography image and at least one of the parameters of the relative electron density, stopping power ratio, and mass density.

[0114] In some embodiments, the device further includes:

[0115] A judgment unit (not shown), which is configured to judge whether there is an abnormality in the radiotherapy plan parameters of the local part of the scanned object; and when there is no radiotherapy plan parameter corresponding to the pixel value of at least one pixel in the acquired computed tomography image pixel in the pre-established association relationship, determine that the radiotherapy plan parameters of the at least one pixel are abnormal.

[0116] In some embodiments, the prompting unit 803 is specifically configured to:

[0117] Generate an abnormality prompt in at least one of the acquired computed tomography image, the radiotherapy plan parameter table, the radiotherapy plan parameter map, or the superimposed map of the acquired computed tomography image and the radiotherapy plan parameter map.

[0118] In some embodiments, the abnormality prompt includes prompting in at least one of the ways of highlighting, thickening the outline, color coding, and prompting box.

[0119] In some embodiments, the medical image includes images of the object to be scanned in multiple planes;

[0120] And the device further includes a display unit (not shown), which is configured to:

[0121] In response to a user's access operation, display the images of each plane included in the medical image and the abnormal data.

[0122] In some embodiments, the device further includes an update unit (not shown), which is configured to:

[0123] In response to a user's operation of updating the look-up table, update the look-up table, and regenerate radiotherapy plan parameters based on the updated look-up table and the computed tomography image.

[0124] In some embodiments, the generating unit 802 is further configured to:

[0125] In response to the user's operation of removing metal artifacts, radiotherapy planning parameters are regenerated based on the computed tomography image.

[0126] Thus, through the medical image processing device of the present application, for a CT image reconstructed based on scan data or an image for radiotherapy planning, the identification of abnormal radiotherapy planning parameters is automatically performed, reducing the user's burden, improving the identification efficiency and the accuracy of the radiotherapy plan; and when abnormal radiotherapy planning parameters are identified, an abnormal prompt is automatically generated to warn the user, indicating that there are abnormal radiotherapy planning parameters. Thus, the user can perform medical image reconstruction based on this prompt, improving the operation efficiency and the accuracy of the radiotherapy plan.

[0127] The embodiment of the present application further provides a medical imaging device, which includes a memory and a processor. The memory stores a computer program, and the processor is configured to execute the computer program to implement the medical image processing method as described in the above embodiments of the present application.

[0128] The embodiment of the present application provides a medical imaging device, which includes the medical image processing device 800 as described in the above embodiments, and its content is incorporated herein.

[0129] The above medical imaging device may be, for example, a computer, a server, a workstation, a laptop computer, a smart phone, etc.; however, the embodiments of the present application are not limited thereto.

[0130] Figure 9 It is a schematic diagram of the medical imaging device according to the embodiment of the present application. As Figure 9 shown, the medical imaging device 900 may include: one or more processors (such as a central processing unit CPU) 910 and one or more memories 920; the memory 920 is coupled to the processor 910. The memory 920 can store various data; in addition, a program 921 for information processing is also stored, and the program 921 is executed under the control of the processor 910.

[0131] In some embodiments, the function of the medical image processing device 800 is integrated into the processor 910. Among them, the processor 910 is configured to implement the medical image processing method as described in the embodiments of the present application.

[0132] In some embodiments, the medical image processing device 800 is separately configured from the processor 910. For example, the medical image processing device 800 can be configured as a chip connected to the processor 910, and the function of the calibration device 800 is realized through the control of the processor 910.

[0133] For example, the processor 910 is configured to perform the following control: obtain a computed tomography image of a scanned object; generate radiotherapy planning parameters, the radiotherapy planning parameters being generated based on the obtained computed tomography image and the association relationship established in advance between the pixel values of each pixel of the computed tomography image and the radiotherapy planning parameters for formulating a radiotherapy plan; generate an abnormal prompt for radiotherapy planning parameters, the abnormal prompt being generated when there is an abnormality in the radiotherapy planning parameters of a local part of the scanned object.

[0134] In addition, as Figure 9 shown, the medical imaging device 900 may further include: an input / output (I / O) device 930, a display 940, etc.; among them, the functions of the above components are similar to those in the prior art and will not be elaborated here. It should be noted that the medical imaging device 900 does not necessarily have to include Figure 9 all the components shown in Figure 9 ; in addition, the medical imaging device 900 may further include

[0135] The embodiment of the present application also provides a computer-readable program, where when the program is executed in an electronic device, the program causes the computer to execute the medical image processing method as described in the above embodiment in the electronic device.

[0136] The embodiment of the present application also provides a storage medium storing a computer-readable program, where the computer-readable program causes the computer to execute the medical image processing method as described in the above embodiment in an electronic device.

[0137] The above device and method of the present application can be implemented by hardware or by a combination of hardware and software. The present application relates to such a computer-readable program that when the program is executed by a logic component, it can cause the logic component to implement the device or component described above, or cause the logic component to implement the various methods or steps described above. The present application also relates to a storage medium for storing the above program, such as a hard disk, a magnetic disk, an optical disk, a DVD, a flash memory, etc.

[0138] The method / device described in combination with the embodiment of the present application can be directly embodied as hardware, a software module executed by a processor, or a combination of the two. For example, one or more of the functional block diagrams shown in the figure and / or a combination of one or more of the functional block diagrams can correspond to each software module of the computer program flow, and can also correspond to each hardware module. These software modules can respectively correspond to the various steps shown in the figure. These hardware modules can be implemented by solidifying these software modules using a field programmable gate array (FPGA), for example.

[0139] A software module can be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. A storage medium can be coupled to a processor such that the processor can read information from the storage medium and write information to the storage medium; or the storage medium can be a component of the processor. The processor and the storage medium can be located in an ASIC. The software module can be stored in the memory of a mobile terminal or in a memory card that can be inserted into the mobile terminal. For example, if a device (such as a mobile terminal) uses a large-capacity MEGA-SIM card or a large-capacity flash memory device, the software module can be stored in the MEGA-SIM card or the large-capacity flash memory device.

[0140] One or more of the functional blocks described in the accompanying drawings and / or one or more combinations of the functional blocks can be implemented as a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or any suitable combination thereof for performing the functions described in this application. One or more of the functional blocks described in the accompanying drawings and / or one or more combinations of the functional blocks can also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication combination with a DSP, or any other such configuration.

[0141] Each of the above embodiments only exemplarily illustrates the embodiments of this application, but this application is not limited thereto, and appropriate modifications can also be made on the basis of each of the above embodiments. For example, each of the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0142] The above describes this application in combination with specific implementation manners, but those skilled in the art should understand that these descriptions are exemplary and not a limitation on the protection scope of this application. Those skilled in the art can make various modifications and variations to this application according to the spirit and principle of this application, and these modifications and variations are also within the scope of this application.

[0143] The preferred embodiments of the present application have been described above with reference to the accompanying drawings. Many features and advantages of these embodiments are apparent from this detailed description, and thus the appended claims are intended to cover all such features and advantages that fall within the true spirit and scope of these embodiments. In addition, since many modifications and variations are readily contemplated by those skilled in the art, the embodiments of the present application are not to be limited to the exact structures and operations illustrated and described, but may cover all suitable modifications, variations, and equivalents that fall within their scope.

Claims

1. A medical image processing method, characterized in that, The method includes: Obtaining a computed tomography (CT) image of a scanned object; Generating radiotherapy plan parameters, where the radiotherapy plan parameters are generated based on the obtained CT image and a pre-established association relationship between the pixel values of each pixel in the CT image and the radiotherapy plan parameters for formulating a radiotherapy plan; Generating an abnormal prompt for radiotherapy plan parameters, where the abnormal prompt is generated when there is an abnormality in the radiotherapy plan parameters in a local part of the scanned object.

2. The medical image processing method according to claim 1, wherein The generating of the radiotherapy plan parameters includes: Generating a radiotherapy plan parameter table or generating a radiotherapy plan parameter graph.

3. The medical image processing method according to claim 2, wherein The radiotherapy plan parameters include at least one of relative electron density, stopping power ratio, and mass density.

4. The medical image processing method according to claim 3, wherein The association relationship includes the correspondence between the CT value of each pixel in the CT image and at least one of the relative electron density, stopping power ratio, and mass density.

5. The medical image processing method according to claim 2, characterized in that, The method further includes: Judging whether there is an abnormality in the radiotherapy plan parameters in a local part of the scanned object; and When there is no radiotherapy plan parameter corresponding to the pixel value of at least one pixel in the pixels of the obtained CT image in the pre-established association relationship, determining that the radiotherapy plan parameters of the at least one pixel are abnormal.

6. The medical image processing method according to claim 5, characterized in that The generating of the abnormal prompt for radiotherapy plan parameters includes: Generating an abnormal prompt in at least one of the obtained CT image, the radiotherapy plan parameter table, the radiotherapy plan parameter graph, or an overlay graph of the obtained CT image and the radiotherapy plan parameter graph.

7. The medical image processing method according to claim 6, wherein The abnormality Prompt includes prompting by at least one of highlighting, bolding the outline, color coding, and tooltips.

8. A medical image processing device, characterized in that, The device includes: An obtaining unit configured to obtain a CT image of a scanned object; A generating unit configured to generate radiotherapy plan parameters, where the radiotherapy plan parameters are generated based on the obtained CT image and a pre-established association relationship between the pixel values of each pixel in the CT image and the radiotherapy plan parameters for formulating a radiotherapy plan; A prompting unit configured to generate an abnormal prompt for radiotherapy plan parameters, where the abnormal prompt is generated when there is an abnormality in the radiotherapy plan parameters in a local part of the scanned object.

9. The medical image processing apparatus according to claim 8, wherein The generating unit is specifically configured to: Generate a radiotherapy plan parameter table or generate a radiotherapy plan parameter graph.

10. The medical image processing device according to claim 9, wherein The radiotherapy plan parameters include at least one of relative electron density, stopping power ratio, and mass density.

11. The medical image processing apparatus according to claim 10, wherein The association relationship includes the correspondence between the CT value of each pixel in the CT image and at least one of the relative electron density, stopping power ratio, and mass density.

12. The medical image processing apparatus according to claim 9, wherein, The device further includes: A judging unit configured to judge whether there is an abnormality in the radiotherapy plan parameters in a local part of the scanned object; and When there is no radiotherapy plan parameter corresponding to the pixel value of at least one pixel in the pixels of the obtained CT image in the pre-established association relationship, determining that the radiotherapy plan parameters of the at least one pixel are abnormal.

13. The medical image processing apparatus according to claim 12, wherein The prompting unit is specifically configured to: Generate an abnormality prompt in at least one of the obtained computed tomography image, the radiotherapy plan parameter table, the radiotherapy plan parameter graph, or the superimposed graph of the obtained computed tomography image and the radiotherapy plan parameter graph.

14. The medical image processing apparatus according to claim 13, wherein The abnormality Prompt includes prompting by at least one of highlighting, thickening the outline, color coding, and tooltips.

15. A medical imaging device, characterized in that, The medical imaging device includes a memory and a processor. The memory stores a computer program, and the processor is configured to execute the computer program to implement the medical image processing method according to any one of claims 1 to 7.

Images