Medical imaging method and device and medical imaging equipment

Abstract

The embodiment of the invention provides a medical imaging method and device and medical imaging equipment. The method comprises the following steps: acquiring a region of interest on a reconnaissance image of an examination object by using a deep learning method; determining a geometric center of an organ of interest of the examination object according to the region of interest; and driving the patient table to move according to the geometric center of the organ of interest and the geometric rotation center of medical imaging equipment. According to the embodiment of the invention, the automatic movement of the patient table is realized, so that the geometric center of the organ of interest is close to the geometric rotation center of the medical imaging equipment, the diagnostic image has higher image quality, including higher image resolution, meanwhile, rays penetrating through the geometric rotation center of the medical imaging equipment are fully utilized, and correspondingly, the image quality is improved. And the dose efficiency of the rays is higher.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of medical equipment technology, and in particular, to a medical imaging method, apparatus, and medical imaging equipment. Background Art

[0002] CT (Computed Tomography) is a computer tomography scan that uses precisely collimated X-ray beams and gamma rays, together with extremely sensitive detectors, to perform cross-sectional scans around a certain part of the human body one after another. It has the characteristics of fast scanning time and clear images, and can be used to detect a variety of diseases. According to the different rays used, it can be divided into: X-ray CT (X-CT) and gamma-ray CT (γ-CT), etc.

[0003] CT scans have many advantages. First, they can obtain high-density and high-resolution images, allowing doctors to clearly observe the size, shape, location and other information of the lesion. Second, CT scans are non-invasive to the human body, with relatively low radiation doses, and are safer for patients. In addition, CT scans are fast and are suitable for scenarios that require rapid diagnosis, such as emergency treatment.

[0004] CT scans have a wide range of clinical applications. In the diagnosis of head and neck diseases, CT scans can help doctors observe the skull and soft tissue structure and detect diseased tissue. In the diagnosis of chest diseases, CT scans can observe the structure of the lungs and heart and detect diseases such as lung cancer, pneumonia, and pneumothorax. In the diagnosis of abdominal diseases, CT scans can observe the structure of organs such as the liver, spleen, and kidneys and detect diseases such as tumors and stones. In addition, CT scans can also play a role in the diagnosis of bone and spinal diseases.

[0005] It should be noted that the above introduction to the technical background is only intended to provide a clear and complete description of the technical solution of the present application and to facilitate understanding by those skilled in the art. Summary of the invention

[0006] The inventors found that during the CT scanning process, the patient (examinee) must first be placed on a patient table (also called a scanning bed or CT bed, etc.), and then the patient table must be manually operated, such as moving the patient table forward and backward or up and down, with the aid of a laser light for positioning. However, since the examined part, such as the heart, spine, etc., is located in the patient's body, its position varies from patient to patient and is not the geometric center of the patient's outline. Therefore, whether the patient table is moved forward and backward or up and down, the examined part (i.e., the organ of interest) cannot be moved to the geometric rotation center of the medical imaging device (i.e., the CT rack). That is, manually operating the patient table results in low operating efficiency and low accuracy of movement.

[0007] In response to at least one of the above technical problems or other similar problems, the embodiments of the present application provide a medical imaging method, apparatus and medical imaging equipment to achieve automatic movement of a patient table and improve operating efficiency and movement accuracy.

[0008] According to one aspect of an embodiment of the present application, a medical imaging method is provided, the method comprising:

[0009] Using a deep learning method to obtain a region of interest on a reconnaissance image of an inspection object;

[0010] Determining the geometric center of the organ of interest of the examination object according to the region of interest;

[0011] The patient table is driven to move according to the geometric center of the organ of interest and the geometric rotation center of the medical imaging device.

[0012] According to another aspect of an embodiment of the present application, a medical imaging device is provided, the device comprising:

[0013] an acquisition unit that acquires a region of interest on a scout image of an inspection object using a deep learning method;

[0014] a first determining unit, which determines a geometric center of an organ of interest of the examination object according to the region of interest;

[0015] A control unit drives the patient table to move according to the geometric center of the organ of interest and the geometric rotation center of the medical imaging device.

[0016] According to another aspect of an embodiment of the present application, a medical imaging device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the medical imaging method as described above.

[0017] One of the beneficial effects of the embodiments of the present application is that: according to the embodiments of the present application, automatic movement of the patient table is realized, thereby bringing the geometric center of the organ of interest close to the geometric rotation center of the medical imaging device, so that the diagnostic image has higher image quality, including higher image resolution. At the same time, the rays passing through the geometric rotation center of the medical imaging device are fully utilized, and accordingly, the dose efficiency of the rays is higher.

[0018] With reference to the following description and accompanying drawings, the specific implementation of the embodiment of the present application is disclosed in detail, indicating the way in which the principle of the embodiment of the present application can be adopted. It should be understood that the implementation of the present application is not limited in scope. Within the scope of the spirit and clauses of the attached claims, the implementation of the present application includes many changes, modifications and equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The included drawings are used to provide a further understanding of the embodiments of the present application, which constitute a part of the specification, are used to illustrate the implementation methods of the present application, and together with the text description, explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other implementation methods can be obtained based on these drawings without creative work. In the drawings:

[0020] Figure 1 is a schematic diagram of a CT imaging device according to an embodiment of the present application;

[0021] Figure 2 is a schematic diagram of a CT imaging system according to an embodiment of the present application;

[0022] Figure 3 is a schematic diagram of a medical imaging method according to an embodiment of the present application;

[0023] Figure 4 is a schematic diagram of a reconnaissance image of a chest;

[0024] Figure 5 is another schematic representation of a reconnaissance image of the chest;

[0025] Figure 6 The schematic diagram of the bounding box B1 of the organ of interest is obtained by resizing and normalizing the input scout image and then extracting features from the scout image through a deep learning network;

[0026] Figure 7 It is a schematic diagram of extracting features from an input reconnaissance image through a deep learning network to obtain a contour C1 of an organ of interest, and then transforming the contour C1 to obtain a bounding box B2 of the organ of interest;

[0027] Figure 8 is based on Figure 4 A schematic diagram of a bounding box of an organ of interest obtained from a 0 degree scout image is shown;

[0028] Fig. 9 is based on Figure 5 A schematic diagram of a bounding box of an organ of interest obtained from a 90 degree scout image is shown;

[0029] Fig.10 is a schematic diagram of a center line obtained by intersecting a tangent plane of the center line of the first bounding box and a tangent plane of the center line of the second bounding box;

[0030] Fig.11 It is a schematic diagram showing the vision adjustment strategy;

[0031] Fig.12is a schematic diagram of a medical imaging device according to an embodiment of the present application;

[0032] Fig.13 is a schematic diagram of a medical imaging device according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] With reference 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 the accompanying 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 that fall within the scope of the attached claims.

[0034] In the embodiments of the present application, the terms "first", "second", etc. are used to distinguish different elements in terms of title, but do not indicate the spatial arrangement or temporal order of these elements, etc., 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. The terms "comprising", "including", "having", etc. refer to the presence of the stated features, elements, components or components, but do not exclude the presence or addition of one or more other features, elements, components or components.

[0035] In the embodiments of the present application, the singular forms "a", "the", etc. include plural forms and should be broadly understood as "a kind" or "a type" rather than being limited to the meaning of "one"; in addition, the term "said" should be understood to include both singular and plural forms, unless the context clearly indicates otherwise. In addition, the term "according to" should be understood as "at least in part according to...", and the term "based on" should be understood as "at least in part based on...", unless the context clearly indicates otherwise.

[0036] Features described and / or shown for one embodiment may be used in one or more other embodiments in the same or similar manner, combined with features in other embodiments, or replace features in other embodiments. The term "includes / comprising" when used herein refers to the presence of a feature, an entirety, a step, or a component, but does not exclude the presence or addition of one or more other features, entireties, steps, or components.

[0037] The device for obtaining medical imaging data described in this article can be applicable to various medical imaging modalities, including but not limited to CT (computed tomography) equipment, PET (positron emission tomography)-CT or any other suitable medical imaging equipment.

[0038] The system for obtaining medical imaging data may include the aforementioned medical imaging device, or may include a separate computer device connected to the medical imaging device, or may include a computer device connected to the Internet cloud, the computer device being connected to the medical imaging device or a storage device storing medical images via the Internet. The imaging method may be implemented independently or jointly by the aforementioned medical imaging device, the computer device connected to the medical imaging device, and the computer device connected to the Internet cloud.

[0039] Exemplarily, the embodiments of the present application are described below in conjunction with an X-ray computed tomography (CT) device. Those skilled in the art will appreciate that the embodiments of the present application may also be applicable to other medical imaging devices.

[0040] Figure 1 FIG. 1 is a schematic diagram of a CT imaging device according to an embodiment of the present application, schematically illustrating the CT imaging device 100. Figure 1 As shown, the CT imaging 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 toward a collimator or a detector assembly 104 on the opposite side of the scanning gantry 101. A test object 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 test object 105 can be obtained.

[0041] Figure 2 2 is a schematic diagram of a CT imaging system according to an embodiment of the present application, schematically showing a block diagram of a CT imaging system 200. Figure 2 As shown, the detector assembly 104 includes a plurality of detector units 104 a and a data acquisition system (DAS) 104 b. The plurality of detector units 104 a sense the projected X-rays passing through the inspection object 105.

[0042] The DAS 104b converts the collected information into projection data for subsequent processing based on the sensing of the detector unit 104a. During the scanning period for collecting X-ray projection data, the scanning gantry 101 and the components mounted thereon rotate around the rotation center 101c.

[0043] 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 large-capacity storage device 206.

[0044] The computer 205 also receives commands and scanning parameters from an operator via a console 207. The console 207 has some form of operator interface, such as a keyboard, mouse, voice activated controller, or any other suitable input device. An associated display 208 allows the operator to observe reconstructed images 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. In addition, the computer 205 operates the patient table motor controller 209, which controls the patient table 102 to position the test subject 105 and the scanning gantry 101. In particular, the patient table 102 moves the test subject 105, in whole or in part, through the scanning gantry 101. Figure 1 The scanning gantry opening 106 is provided.

[0045] The above schematically illustrates the device and system for acquiring medical imaging data (or also referred to as medical images or medical image data) of an embodiment of the present application, but the present application is not limited thereto. The medical imaging device may be a CT device, a PET-CT device, or any other suitable imaging device. The storage device may be located in the medical imaging device, in a server outside the medical imaging device, in an independent medical imaging storage system (such as PACS, Picture Archiving and Communication System), and / or in a remote cloud storage system.

[0046] In addition, the medical imaging workstation can be set up locally in the medical imaging device, that is, the medical imaging workstation is set up near the medical imaging device, and the two can be located together in the scanning room, imaging department or the same hospital. The medical image cloud platform analysis system can be located away from the medical imaging device, for example, in the cloud that communicates with the medical imaging device.

[0047] As an example, after a medical institution completes an imaging scan using medical imaging equipment, the scanned data is stored in a storage device; a medical imaging workstation can directly read the scanned data and perform image processing through its processor. As another example, a medical image cloud platform analysis system can read medical images in a storage device through remote communication to provide "Software as a Service" (SAAS). SAAS can exist between hospitals, between hospitals and imaging centers, or between hospitals and third-party online diagnosis and treatment service providers.

[0048] The above schematically illustrates medical image scanning, and the embodiments of the present application are specifically described below with reference to the accompanying drawings.

[0049] Embodiments of the first aspect

[0050] An embodiment of the present application provides a medical imaging method. Figure 3 is a schematic diagram of a medical imaging method according to an embodiment of the present application. Figure 3 As shown, the method includes:

[0051] 310: Obtaining a region of interest on a reconnaissance image of an inspection object using a deep learning method;

[0052] 320: Determine a geometric center of an organ of interest of the examination object according to the region of interest;

[0053] 330: Drive the patient table to move according to the geometric center of the organ of interest and the geometric rotation center of the medical imaging device.

[0054] It is worth noting that the above Figure 3 The embodiments of the present application are only schematically described, but the present application is not limited thereto. For example, other operations may be added or some operations may be reduced. Those skilled in the art may make appropriate modifications based on the above content, and are not limited to the above attached embodiments. Figure 3 Records of.

[0055] In the above embodiment, the geometric center of the organ of interest of the examination object is determined according to the region of interest on the scout image, and the patient table is driven to move according to the geometric center of the organ of interest and the geometric rotation center of the medical imaging device, thereby realizing automatic movement of the patient table. As a result, the geometric center of the organ of interest is close to the geometric rotation center of the medical imaging device (i.e., the CT rack), so that the diagnostic image has higher image quality, including higher image resolution. At the same time, the rays passing through the geometric rotation center of the medical imaging device are fully utilized, and accordingly, the dose efficiency of the rays is higher.

[0056] In the embodiment of the present application, the scout image of the inspection object is obtained by performing a CT scan on the inspection part of the inspection object through a medical imaging device, such as a CT device.

[0057] In some embodiments, in step 310, the regions of interest on at least two scout images taken at different angles are obtained, that is, two scout images with different imaging angles are used as inputs of the network model adopted by the deep learning method to obtain the regions of interest respectively. For example, two scout images with imaging angles of 0 degrees and 90 degrees are used, but the present application is not limited thereto, and two scout images with imaging angles of other angles may also be used, or more than two scout images may be used to obtain the regions of interest respectively.

[0058] Figure 4 is a schematic diagram of a reconnaissance image of the chest. Figure 4 In the example, the imaging angle is 0 degrees; Figure 5 is another schematic diagram of a reconnaissance image of the chest, Figure 5 In the example shown, the imaging angle is 90 degrees. Figure 4 and Figure 5 In the example, the chest of the inspection object is taken as the inspection part, but the present application is not limited to this. The inspection part may also be other parts of the inspection object, such as legs, back, etc.

[0059] In the above embodiment, there is no limitation on the network model (i.e., deep learning network) adopted by the deep learning method. For example, a convolutional neural network model, a deep trust network model, a stacked autoencoder network model, etc. may be adopted. A typical example is a trained U-net. For details, please refer to the relevant technology, and the description is omitted here.

[0060] In some embodiments, before using the deep learning method to obtain the region of interest on the scout image of the inspection object, the scout image may be resized and / or normalized so that the processed scout image can adapt to the architecture and requirements of the network model. However, the present application is not limited thereto. In some embodiments, the input scout image itself can already meet the requirements of the network model. In this case, there is no need to perform resizing / normalization processing on the scout image.

[0061] In some embodiments, the region of interest obtained by the deep learning method is a bounding box of the organ of interest, that is, the bounding box of the organ of interest can be directly extracted by the deep learning method;

[0062] Figure 6 The input reconnaissance image is resized and normalized, and then the feature of the reconnaissance image is extracted through a deep learning network to obtain a bounding box B1 of the organ of interest.

[0063] In other embodiments, the region of interest obtained by the deep learning method is the contour of the organ of interest, that is, the contour of the organ of interest can be obtained by the deep learning method. In this embodiment, the contour of the organ of interest can also be converted to obtain a bounding box of the organ of interest. The present application does not limit the specific conversion method. For example, the maximum boundary values ​​of the upper, lower, left, and right sides of the contour can be taken as a vertical line to obtain the corresponding bounding box. For another example, the sum of the maximum boundary values ​​of the upper, lower, left, and right sides of the contour and a predetermined value can be taken as the bounding box corresponding to the contour, and so on.

[0064] Figure 7 It is a schematic diagram of extracting features from an input reconnaissance image through a deep learning network to obtain a contour C1 of an organ of interest, and then transforming the contour C1 to obtain a bounding box B2 of the organ of interest.

[0065] exist Figure 6 and Figure 7 In the example, Figure 4 Take the 0 degree reconnaissance image shown as an example, for Figure 5 The 90-degree reconnaissance image shown can also be processed similarly Figure 6 or Figure 7 The bounding box of the organ of interest is obtained.

[0066] Figure 8 is based on Figure 4 Schematic diagram of the bounding box of the organ of interest obtained from the 0 degree scout image shown, Fig. 9 is based on Figure 5 A schematic diagram of the bounding box of the organ of interest obtained from the 90-degree scout image is shown.

[0067] like Figure 8 As shown, the deep learning method and the deep learning network are used to obtain the boundaries L and R of the bounding box of the organ of interest in the x direction (the left and right movement direction of the patient table), where L represents the left side of the examination object and R represents the right side of the examination object. Figure 8 In the example shown in FIG. 1 , the direction passing through the paper is the y direction, that is, the up and down movement direction of the patient table.

[0068] like Fig. 9 As shown, the deep learning method and the deep learning network are used to obtain the boundaries A and P of the bounding box of the organ of interest in the y direction (the up and down movement direction of the patient table), where A represents the upper side or front side of the object under examination, and P represents the lower side or back side of the object under examination. Fig. 9 In the example of FIG. 1 , the direction passing through the paper is the x direction, that is, the left-right moving direction of the patient table.

[0069] In addition, Figure 8 and Fig. 9 In the example, the starting position and the ending position of the organ of interest in the z direction (the forward and backward movement direction of the patient table) (ie, the direction in which the patient table carrying the examination object moves into or out of the scanning gantry opening 106) are also indicated.

[0070] In some embodiments, in step 320 , a geometric center of the organ of interest of the examination object may be determined according to a bounding box of the organ of interest.

[0071] For example, a line obtained by intersecting a tangent plane of a center line of a first bounding box obtained from a first scout image of the object under examination and a tangent plane of a center line of a second bounding box obtained from a second scout image of the object under examination may be used as the geometric center of the organ of interest of the object under examination. The tangent plane of the center line of the first bounding box refers to a cross section of the center line of the first bounding box in the x direction in the y direction; the tangent plane of the center line of the second bounding box refers to a cross section of the center line of the second bounding box in the y direction in the x direction.

[0072] by Figure 8 and Fig. 9 Take the two bounding boxes shown in Figure 8 As the first scout image, Figure 8 The bounding box B1 shown is used as the first bounding box. Fig. 9 As the second scout image, Fig. 9 The bounding box B2 shown is used as the second bounding box. Then the center line of the bounding box B1 refers to the line that divides the bounding box B1 equally in the x direction, denoted as L1; similarly, the center line of the bounding box B2 refers to the line that divides the bounding box B2 equally in the y direction, denoted as L2. Then the tangent plane of the center line L1 of the bounding box B1 refers to the cross section of the center line L1 in the y direction, denoted as S1, as shown in Fig.10 Similarly, the tangent plane of the center line L2 of the bounding box B2 refers to the cross section of the center line L2 in the x direction, denoted as S2, as shown in Fig.10 In the embodiment of the present application, the geometric center of the organ of interest of the inspection object is the line obtained by the intersection of S1 and S2, as shown in FIG. Fig.10 L3 shown.

[0073] In the above embodiment, the patient table is driven to move in such a way that the geometric center of the organ of interest (that is, the above center line L3) is close to the geometric rotation center of the medical imaging device (for example, the rotation center of the CT rack). Thus, the geometric center of the organ of interest can be brought as close to the geometric rotation center of the medical imaging device as possible, thereby obtaining a diagnostic image with higher image quality and image resolution. At the same time, rays passing through the geometric rotation center of the medical imaging device are fully utilized, thereby increasing the dose efficiency of the rays.

[0074] In the above embodiment, the reconstruction center and display field of view of the diagnostic image of the inspection object may also be determined according to the bounding box of the organ of interest (eg, the first bounding box and the second bounding box).

[0075] For example, the coordinate origin (X, Y) of the reconstruction center of the diagnostic image is: the center of the first bounding box in the x direction (the center of R and L) and the center of the second bounding box in the y direction (the center of P and A). It can be expressed as follows:

[0076] (X,Y)=((RL) / 2,(PA) / 2);

[0077] For another example, the value of the display field of view DFOV of the diagnostic image is the greater of the absolute value of the difference between the two boundary positions in the x direction of the first boundary box (abs(RL)) and the absolute value of the difference between the two boundary positions in the y direction of the second boundary box (abs(PA)). It can be expressed as follows:

[0078] DFOV=Max(abs(RL),abs(PA)).

[0079] In some embodiments, in step 320, the geometric center of the organ of interest of the examination object may be determined according to the contour of the organ of interest.

[0080] For example, a line obtained by intersecting a section of a center line of a first contour obtained from a first scout image of the object under examination and a section of a center line of a second contour obtained from a second scout image of the object under examination can be used as the geometric center of the organ of interest of the object under examination. The section of the center line of the first contour refers to a cross section of the center line of the first contour in the x direction in the y direction; the section of the center line of the second contour refers to a cross section of the center line of the second contour in the y direction in the x direction.

[0081] Based on Figure 4 The contour C1 of the organ of interest obtained from the scout image shown (see Figure 7 The contour C1) shown and based on Figure 5 Taking the contour C2 (not shown in the figure) of the organ of interest obtained from the scout image as an example, Figure 4 As the first scout image, based on Figure 4 The obtained contour C1 is used as the first contour. Figure 5 As the second scout image, based on Figure 5 The obtained contour C2 is used as the second contour.

[0082] In the above embodiment, the center line of the contour C1 refers to the center line between the two side boundaries of the contour C1 in the x direction, and the tangent plane of the center line of the contour C1 refers to the cross section of the above center line in the y direction; similarly, the center line of the contour C2 refers to the center line between the two side boundaries of the contour C2 in the y direction, and the tangent plane of the center line of the contour C2 refers to the cross section of the above center line in the x direction.

[0083] In the above embodiment, the reconstruction center and display field of view of the diagnostic image of the inspection object may also be determined according to the contour of the organ of interest (eg, the first contour and the second contour).

[0084] For example, the center of the leftmost boundary (most L) and the rightmost boundary (most R) in the x direction of the contour C1 is used as the coordinate X of the reconstruction center, and the center of the frontmost boundary (most A) and the rearmost boundary (most P) in the y direction of the contour C2 is used as the coordinate Y of the reconstruction center. It can be expressed as follows:

[0085] (X,Y)=((most R-most L) / 2, (most P-most A) / 2).

[0086] For another example, the greater of the absolute value of the difference between the leftmost boundary position and the rightmost boundary position in the x direction of the contour C1 (abs(most R-most L)) and the absolute value of the difference between the leftmost boundary position and the rightmost boundary position in the x direction of the contour C2 (abs(most P-most A)) is used as the value of the display field of view DFOV. It can be expressed as follows:

[0087] DFOV=Max(abs(most R-most L),abs(most P-most A)).

[0088] The above two examples are only illustrative. Depending on the different reconnaissance images and / or the different regions of interest obtained based on the reconnaissance images, other methods can be used to obtain the re-center and display field of view based on the principles of the above examples. For example, the reconstruction center can be obtained by calculating the pattern center or the center of gravity, and then the corresponding display field of view can be obtained based on the obtained reconstruction center, and so on.

[0089] In the embodiments of the present application, in some embodiments, the inspection object is not located at or close to the geometric rotation center of the medical imaging device, but is offset from the geometric rotation center of the medical imaging device, which will result in an inaccurate display field of view of the obtained diagnostic image. Therefore, it is necessary to design an adjustment coefficient of the display field of view to adjust the display field of view.

[0090] Fig.11is a schematic diagram showing the vision adjustment strategy, such as Fig.11 As shown on the left side of FIG, there is an offset between the position RL of the inspection object and the reconstruction center, which results in a relatively large display field of view DL1 of the obtained diagnostic image. In fact, as shown in FIG. Fig.11 As shown on the right side of , when the object under examination is located at the re-center, the display field of view DL2) of the obtained diagnostic image is smaller and more accurate than DL1.

[0091] In the above embodiment, another scout image (such as Fig.11 ), and obtain the current reconnaissance image (as shown on the right side of Fig.11 The offset value offset) is shown on the left side of , from which the adjustment coefficient M of DFOV can be obtained.

[0092] For example, the adjustment coefficient M of the display field of view may be determined according to the distance (F1) between the X-ray tube and the reconstruction center of the diagnostic image and the distance (offset) between the inspection object and the reconstruction center of the diagnostic image.

[0093] In the above embodiment, the adjustment coefficient M of the display field of view can be expressed as: the difference between the distance (F1) from the X-ray tube to the reconstruction center of the diagnostic image and the distance (offset) from the inspection object to the reconstruction center of the diagnostic image divided by the distance (F1) from the X-ray tube to the reconstruction center of the diagnostic image. It can be expressed as follows:

[0094] M=DL2 / DL1=(F1-Offset) / F1

[0095] In the above embodiment, the updated value of the display field of view may be determined according to the adjustment coefficient M of the display field of view.

[0096] For example, still Figure 8 and Fig. 9 Taking the reconnaissance image and bounding boxes B1 and B2 as an example, after introducing the adjustment coefficient M of the display field of view, the calculation formula of the display field of view is changed to the following:

[0097] DFOV=Max(M1×abs(RL),M2×abs(PA))

[0098] Among them, M1 is Figure 8 The adjustment factor of the display field of view corresponding to the reconnaissance image shown, M2 is Fig. 9 The modulation factor of the displayed field of view corresponding to the scout image shown.

[0099] In the above embodiment, M1 and M2 may be the same or different, that is, the adjustment coefficients of the display fields of different reconnaissance images may be the same or different.

[0100] In the embodiment of the present application, the allowed movement range of the patient table is different due to the different heights, weights and sizes of the subjects to be examined. In some embodiments, the relationship between the height of the patient table and the allowed movement range can be preset, and then, based on the relationship and the distance between the geometric center of the organ of interest and the geometric rotation center of the medical imaging device, the movable range of the patient table is determined, and then, it is determined whether the movement of the patient table exceeds the limit of the allowed movement, and the control unit drives the patient table to move based on the judgment result, so that the movement of the patient table does not exceed the limit of the allowed movement.

[0101] In the above embodiment, the above relationship may be expressed as a table. For example, the relationship is a configuration file in a table form. The configuration file includes a plurality of patient tables and an allowable movement range corresponding to the height of each patient table.

[0102] In the above embodiment, the above relationship may also be expressed as a fitting function of the height of the patient table and the allowed movement range, whereby the corresponding allowed movement range may also be determined according to the height of the patient table.

[0103] According to the above embodiment, since the relationship between the height of the patient table and the allowable movement range is taken into consideration, the patient table is prevented from interfering with the gantry bore during movement, thereby further improving operability.

[0104] The above embodiments are merely exemplary of the embodiments of the present application, but the present application is not limited thereto, and appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0105] According to the embodiment of the present application, automatic movement of the patient table is achieved, whereby the geometric center of the organ of interest is close to the geometric rotation center of the medical imaging device, so that the diagnostic image has higher image quality, including higher image resolution. At the same time, the rays passing through the geometric rotation center of the medical imaging device are fully utilized, and accordingly, the dose efficiency of the rays is higher.

[0106] Embodiments of the second aspect

[0107] An embodiment of the present application provides a medical imaging device, and the contents that are the same as those of the embodiment of the first aspect are not repeated here.

[0108] Fig.12 Schematic diagram of a medical imaging device according to an embodiment of the present application. Fig.12 As shown, the medical imaging device 1200 of the embodiment of the present application includes:

[0109] An acquisition unit 1210, which uses a deep learning method to acquire a region of interest on a scout image of an inspection object;

[0110] A first determining unit 1220, which determines a geometric center of an organ of interest of the examination object according to the region of interest;

[0111] The control unit 1230 drives the patient table to move according to the geometric center of the organ of interest and the geometric rotation center of the medical imaging device.

[0112] In some embodiments, the acquisition unit 1210 acquires the region of interest on at least two scout images taken at different angles.

[0113] In some embodiments, Fig.12 As shown, the device 1200 also includes:

[0114] The processing unit 1240 performs size transformation and / or normalization processing on the scout image of the inspection object; the acquisition unit 1210 acquires the region of interest from the scout image processed by the processing unit 1240 .

[0115] In some embodiments, acquiring the region of interest includes: acquiring a bounding box of the organ of interest of the detection object.

[0116] In some embodiments, acquiring the region of interest includes: acquiring the contour of the organ of interest.

[0117] In the above embodiment, the acquisition unit 1210 may also transform the outline of the organ of interest to obtain a bounding box of the organ of interest.

[0118] In some embodiments, the first determining unit 1220 determines the geometric center of the organ of interest according to the bounding box of the organ of interest. For example, the geometric center of the organ of interest is:

[0119] A line obtained by intersecting a tangent plane of a center line of a first bounding box obtained from a first scout image of the inspection object and a tangent plane of a center line of a second bounding box obtained from a second scout image of the inspection object, wherein the tangent plane of the center line of the first bounding box refers to a cross section of the center line of the first bounding box in the x direction in the y direction; the tangent plane of the center line of the second bounding box refers to a cross section of the center line of the second bounding box in the y direction in the x direction; the x direction is a left-right moving direction of the patient table, and the y direction is an up-and-down moving direction of the patient table.

[0120] In the above embodiment, the first determination unit 1220 can also determine the reconstruction center and display field of view of the diagnostic image of the inspection object based on the bounding box of the organ of interest. For example, the first determination unit 1220 determines the reconstruction center and display field of view of the diagnostic image of the inspection object based on the above-mentioned first bounding box and the above-mentioned second bounding box.

[0121] In the above embodiment, the center of the first bounding box in the x direction and the center of the second bounding box in the y direction are used as the coordinate origin of the reconstruction center of the diagnostic image; the larger of the absolute value of the difference between the two side boundary positions of the first bounding box in the x direction and the absolute value of the difference between the two side boundary positions of the second bounding box in the y direction is used as the value of the display field of view.

[0122] In some embodiments, the first determination unit 1220 determines the geometric center of the organ of interest of the inspection object according to the contour of the organ of interest. For example, the geometric center of the organ of interest of the inspection object is: a line obtained by intersecting a section of a center line of a first contour obtained from a first scout image of the inspection object and a section of a center line of a second contour obtained from a second scout image of the inspection object, the section of the center line of the first contour refers to a cross section of the center line of the first contour in the x direction in the y direction; the section of the center line of the second contour refers to a cross section of the center line of the second contour in the y direction in the x direction; the x direction is a left-right moving direction of the patient table, and the y direction is an up-and-down moving direction of the patient table.

[0123] In the above embodiment, the first determination unit 1220 can also determine the reconstruction center and display field of view of the diagnostic image of the examination object according to the contour of the organ of interest, for example, determine the reconstruction center and display field of view of the diagnostic image of the examination object according to the above first contour and the above second contour.

[0124] In the above embodiment, the center of the first contour in the x-direction and the center of the second contour in the x-direction are used as the coordinate origin of the reconstruction center of the diagnostic image; the larger of the absolute value of the difference between the two side boundary positions of the first contour in the x-direction and the absolute value of the difference between the two side boundary positions of the second contour is used as the value of the display field of view.

[0125] In some embodiments, Fig.12 As shown, the device 1200 also includes:

[0126] The updating unit 1250 determines an adjustment coefficient of the display field of view according to the distance (F1) between the X-ray tube and the reconstruction center of the diagnostic image and the distance (offset) between the inspection object and the reconstruction center of the diagnostic image, and determines the value of the updated display field of view according to the adjustment coefficient of the display field of view.

[0127] In the above embodiment, the adjustment coefficient of the display field of view can be: the difference between the distance (F1) from the X-ray tube to the reconstruction center of the diagnostic image and the distance (offset) from the inspection object to the reconstruction center of the diagnostic image divided by the distance (F1) from the X-ray tube to the reconstruction center of the diagnostic image.

[0128] In the above embodiment, the adjustment coefficients (M1 and M2) for different scout images may be the same or different.

[0129] In some embodiments, Fig.12 As shown, the device 1200 also includes:

[0130] The second determining unit 1260 determines whether the movement of the patient table exceeds the limit of the allowed movement according to the relationship between the preset height of the patient table and the allowed movement range and the determined distance between the geometric center of the organ of interest and the geometric rotation center of the medical imaging device. The control unit 1230 drives the patient table to move in combination with the determination result of the second determining unit, so that the movement of the patient table does not exceed the limit of the allowed movement.

[0131] In the above embodiment, the above relationship may be a configuration file in a table form, and the configuration file includes a plurality of patient tables and an allowable movement range corresponding to the height of each patient table.

[0132] In the above embodiment, the above relationship may also be a fitting function of the patient table height and the allowed movement range.

[0133] To keep it simple, Fig.12 The connection relationship or signal direction between various components or modules is only exemplified, but it should be clear to those skilled in the art that various related technologies such as bus connection can be used. The above-mentioned components or modules can be implemented by hardware facilities such as processors and memories; the embodiments of the present application are not limited to this.

[0134] The above embodiments are merely exemplary of the embodiments of the present application, but the present application is not limited thereto, and appropriate modifications may be made based on the above embodiments. For example, the above embodiments may be used alone, or one or more of the above embodiments may be combined.

[0135] According to the embodiment of the present application, automatic movement of the patient table is achieved, whereby the geometric center of the organ of interest is close to the geometric rotation center of the medical imaging device, so that the diagnostic image has higher image quality, including higher image resolution. At the same time, the rays passing through the geometric rotation center of the medical imaging device are fully utilized, and accordingly, the dose efficiency of the rays is higher.

[0136] Embodiments of the third aspect

[0137] The embodiment of the present application provides a medical imaging device, including the medical imaging device 1200 as described in the embodiment of the second aspect, the contents of which are cited here and are not repeated here. The medical imaging device can be, for example, a computer, a server, a workstation, a laptop computer, a smart phone, etc.; however, the embodiment of the present application is not limited thereto.

[0138] Fig.13 Schematic diagram of a medical imaging device according to an embodiment of the present application. Fig.13 As shown, the medical imaging device 1300 may include: one or more processors (e.g., central processing unit CPU) 1310 and one or more memories 1320; the memory 1320 is coupled to the processor 1310. The memory 1320 may store various data; in addition, it may store a program 1321 for information processing, and execute the program 1321 under the control of the processor 1310.

[0139] In some embodiments, the functions of the medical imaging apparatus 1200 are integrated into the processor 1310. The processor 1310 is configured to implement the medical imaging method as described in the embodiment of the first aspect.

[0140] In some embodiments, the medical imaging device 1200 and the processor 1310 are configured separately. For example, the medical imaging device 1200 can be configured as a chip connected to the processor 1310 , and the functions of the medical imaging device 1200 are realized through the control of the processor 1310 .

[0141] For example, the processor 1310 is configured to perform the following control: using a deep learning method to obtain a region of interest on a reconnaissance image of the object under examination; determining the geometric center of an organ of interest of the object under examination based on the region of interest; and driving the patient table to move based on the geometric center of the organ of interest of the object under examination and the geometric rotation center of the medical imaging device.

[0142] In addition, if Fig.13 As shown, the medical imaging device 1300 may also include: an input / output (I / O) device 1330 and a display 1340, etc.; wherein the functions of the above components are similar to those of the prior art and are not described in detail here. It is worth noting that the medical imaging device 1300 does not necessarily have to include Fig.13 In addition, the medical imaging device 1300 may also include Fig.13 For components not shown in the figure, reference may be made to the related art.

[0143] An embodiment of the present application also provides a computer-readable program, wherein when the program is executed in an electronic device, the program enables a computer to execute the medical imaging method as described in the embodiment of the first aspect in the electronic device.

[0144] An embodiment of the present application also provides a storage medium storing a computer-readable program, wherein the computer-readable program enables a computer to execute the medical imaging method as described in the embodiment of the first aspect in an electronic device.

[0145] The above devices and methods of the present application can be implemented by hardware, or by hardware combined with software. The present application relates to such a computer-readable program, which, when executed by a logic component, enables the logic component to implement the above-mentioned devices or components, or enables 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.

[0146] The method / device described in conjunction with the embodiments of the present application may 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 one or more combinations of the functional block diagrams may correspond to various software modules of the computer program flow or to various hardware modules. These software modules may correspond to the various steps shown in the figure, respectively. These hardware modules may be implemented by solidifying these software modules, for example, using a field programmable gate array (FPGA).

[0147] The software module may 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 may be coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium; or the storage medium may be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The software module may be stored in a 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 may be stored in the MEGA-SIM card or the large-capacity flash memory device.

[0148] For one or more of the functional blocks described in the drawings and / or one or more combinations of functional blocks, it can be implemented as a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component or any appropriate combination thereof for performing the functions described in the present application. For one or more of the functional blocks described in the drawings and / or one or more combinations of functional blocks, it can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0149] The present application is described above in conjunction with specific implementation methods, but it should be clear to those skilled in the art that these descriptions are exemplary and are not intended to limit the scope of protection of the present application. Those skilled in the art can make various modifications and variations to the present application based on the principles of the present application, and these modifications and variations are also within the scope of the present application.

[0150] Regarding the above implementation methods disclosed in the examples of this application, the following notes are also disclosed:

[0151] 1. A medical imaging method, wherein the method comprises:

[0152] Using a deep learning method to obtain a region of interest on a reconnaissance image of an inspection object;

[0153] Determining the geometric center of the organ of interest of the examination object according to the region of interest;

[0154] The patient table is driven to move according to the geometric center of the organ of interest of the examination object and the geometric rotation center of the medical imaging device.

[0155] 2. The method according to Supplement 1, wherein obtaining the region of interest comprises:

[0156] A region of interest is acquired on at least two scout images taken at different angles.

[0157] 3. The method according to Note 1 or 2, wherein the method further comprises:

[0158] Before obtaining a region of interest on a scout image of an inspection object using a deep learning method, the scout image is resized and / or normalized.

[0159] 4. The method according to Note 1, wherein:

[0160] Acquiring the region of interest includes: acquiring a bounding box of the organ of interest of the inspection object.

[0161] 5. The method according to Note 1, wherein:

[0162] Acquiring the region of interest includes: acquiring a contour of an organ of interest of the inspection object.

[0163] 6. The method according to claim 5, wherein the method further comprises:

[0164] The contour of the organ of interest is transformed to obtain a bounding box of the organ of interest.

[0165] 7. The method according to Note 4 or 6, wherein determining the geometric center of the organ of interest of the inspection object according to the region of interest comprises:

[0166] The geometric center of the organ of interest of the inspection object is determined according to the bounding box of the organ of interest, wherein the geometric center of the organ of interest of the inspection object is:

[0167] A line obtained by intersecting a tangent plane of a centerline of a first bounding box obtained from a first scout image of the inspection object and a tangent plane of a centerline of a second bounding box obtained from a second scout image of the inspection object,

[0168] The tangent plane of the center line of the first bounding box refers to the cross section of the center line of the first bounding box in the x direction in the y direction;

[0169] The tangent plane of the center line of the second bounding box refers to a cross section of the center line of the second bounding box in the y direction in the x direction;

[0170] The x direction is the left-right moving direction of the patient table, and the y direction is the up-down moving direction of the patient table.

[0171] 8. The method according to claim 7, wherein the method further comprises:

[0172] determining a reconstruction center and a display field of view of a diagnostic image of the inspection object according to the first bounding box and the second bounding box,

[0173] The center of the first bounding box in the x direction and the center of the second bounding box in the y direction are used as the coordinate origin of the reconstruction center of the diagnostic image;

[0174] The larger one of the absolute value of the difference between the two side boundary positions of the first boundary box in the x direction and the absolute value of the difference between the two side boundary positions of the second boundary box in the y direction is used as the value of the display field of view.

[0175] 9. The method according to Supplement 5, wherein determining the geometric center of the organ of interest of the inspection object according to the region of interest comprises:

[0176] The geometric center of the organ of interest of the inspection object is determined according to the contour of the organ of interest, wherein the geometric center of the organ of interest of the inspection object is:

[0177] a line obtained by intersecting a tangent plane of a centerline of a first contour obtained from a first scout image of the inspection object and a tangent plane of a centerline of a second contour obtained from a second scout image of the inspection object,

[0178] The tangent plane of the center line of the first contour refers to the cross section of the center line of the first contour in the x direction in the y direction;

[0179] The tangent plane of the center line of the second contour refers to the cross section of the center line of the second contour in the y direction in the x direction;

[0180] The x direction is the left-right moving direction of the patient table, and the y direction is the up-down moving direction of the patient table.

[0181] 10. The method according to Note 9, wherein the method further comprises:

[0182] determining a reconstruction center and a display field of view of a diagnostic image of the examination object according to the first contour and the second contour,

[0183] The center of the first contour in the x direction and the center of the second contour in the x direction are used as the coordinate origin of the reconstruction center of the diagnostic image;

[0184] The larger one of the absolute value of the difference between the boundary positions on both sides of the first contour in the x direction and the absolute value of the difference between the boundary positions on both sides of the second contour in the y direction is used as the value of the display field of view.

[0185] 11. The method according to Note 8 or 10, wherein the method further comprises:

[0186] determining an adjustment coefficient of a display field of view according to a distance (F1) between an X-ray tube and a reconstruction center of the diagnostic image and a distance (offset) between an inspection object and a reconstruction center of the diagnostic image;

[0187] The updated value of the display field of view is determined according to the adjustment coefficient of the display field of view.

[0188] 12. The method according to Note 11, wherein:

[0189] The adjustment coefficient of the display field of view is: the difference between the distance (F1) from the X-ray tube to the reconstruction center of the diagnostic image and the distance (offset) from the inspection object to the reconstruction center of the diagnostic image divided by the distance (F1) from the X-ray tube to the reconstruction center of the diagnostic image.

[0190] 13. The method according to Note 11, wherein:

[0191] The adjustment coefficients (M1 and M2) for different scout images may be the same or different.

[0192] 14. The method according to Note 1, wherein the method further comprises:

[0193] Determining whether the movement of the patient table exceeds the limit of the allowed movement according to the relationship between the preset patient table height and the allowed movement range and the determined distance between the geometric center of the organ of interest and the geometric rotation center of the medical imaging device;

[0194] The patient table is driven to move according to the determination result, so that the movement of the patient table does not exceed the limit value of the allowed movement.

[0195] 15. The method according to Note 14, wherein:

[0196] The relationship is a configuration file in a table format, and the configuration file includes a plurality of patient tables and an allowable movement range corresponding to the height of each patient table.

[0197] 16. The method according to Note 14, wherein:

[0198] The relationship is a fitted function of the patient table height and the allowed range of movement.

Claims

1. A medical imaging device, characterized in that: The device comprises: an acquisition unit that acquires a region of interest on a scout image of an inspection object using a deep learning method; a first determining unit, which determines a geometric center of an organ of interest of the examination object according to the region of interest; A control unit drives the patient table to move according to the geometric center of the organ of interest and the geometric rotation center of the medical imaging device.

2. The device according to claim 1, wherein: The acquisition unit acquires the region of interest on at least two scout images taken at different angles.

3. The device according to claim 1 or 2, wherein: The device also includes: a processing unit for performing a size conversion and / or normalization process on the reconnaissance image of the inspection object; The acquisition unit acquires the region of interest from the scout image processed by the processing unit.

4. The device according to claim 1, wherein: Acquiring the region of interest includes: acquiring a boundary box of the organ of interest of the inspection object.

5. The device according to claim 1, wherein: Acquiring the region of interest includes: acquiring the contour of the organ of interest of the inspection object.

6. The device according to claim 5, wherein: The acquisition unit further transforms the contour of the organ of interest to obtain a bounding box of the organ of interest.

7. The device according to claim 4 or 6, wherein: The first determining unit determines the geometric center of the organ of interest of the inspection object according to the bounding box of the organ of interest, wherein the geometric center of the organ of interest of the inspection object is: A line obtained by intersecting a tangent plane of a centerline of a first bounding box obtained from a first scout image of the inspection object and a tangent plane of a centerline of a second bounding box obtained from a second scout image of the inspection object, The tangent plane of the center line of the first bounding box refers to the cross section of the center line of the first bounding box in the x direction in the y direction; The tangent plane of the center line of the second bounding box refers to a cross section of the center line of the second bounding box in the y direction in the x direction; The x direction is the left-right moving direction of the patient table, and the y direction is the up-down moving direction of the patient table.

8. The device according to claim 7, wherein: The first determining unit further determines a reconstruction center and a display field of view of the diagnostic image of the inspection object based on the first bounding box and the second bounding box; Wherein, the coordinate origin of the reconstruction center of the diagnostic image is: the center of the first bounding box in the x direction and the center of the second bounding box in the y direction; The value of the display field of view of the diagnostic image is the larger one of the absolute value of the difference between the two side boundary positions of the first boundary box in the x direction and the absolute value of the difference between the two side boundary positions of the second boundary box in the y direction.

9. The device according to claim 5, wherein: The first determining unit determines the geometric center of the organ of interest of the examination object according to the contour of the organ of interest, wherein the geometric center of the organ of interest of the examination object is: a line obtained by intersecting a tangent plane of a centerline of a first contour obtained from a first scout image of the inspection object and a tangent plane of a centerline of a second contour obtained from a second scout image of the inspection object, The tangent plane of the center line of the first contour refers to the cross section of the center line of the first contour in the x direction in the y direction; The tangent plane of the center line of the second contour refers to the cross section of the center line of the second contour in the y direction in the x direction; The x direction is the left-right moving direction of the patient table, and the y direction is the up-down moving direction of the patient table.

10. The device according to claim 9, wherein: The first determination unit further determines a reconstruction center and a display field of view of the diagnostic image of the inspection object according to the first contour and the second contour; The coordinate origin of the reconstruction center is: the center of the first contour in the x direction and the center of the second contour in the y direction; The value of the display field of view of the diagnostic image is the larger one of the absolute value of the difference between the boundary positions on both sides of the first contour in the x direction and the absolute value of the difference between the boundary positions on both sides of the second contour in the y direction.

11. The device according to claim 8 or 10, wherein: The device also includes: An updating unit determines an adjustment coefficient of a display field of view according to a distance between an X-ray tube and a reconstruction center of the diagnostic image and a distance between an inspection object and the reconstruction center of the diagnostic image, and determines a value of an updated display field of view according to the adjustment coefficient of the display field of view.

12. The device according to claim 11, wherein The adjustment coefficient of the display field of view is: the difference between the distance from the X-ray tube to the reconstruction center of the diagnostic image and the distance from the inspection object to the reconstruction center of the diagnostic image divided by the distance from the X-ray tube to the reconstruction center of the diagnostic image.

13. The device according to claim 11, wherein: The adjustment coefficients for different reconnaissance images may be the same or different.

14. The device according to claim 1, wherein: The device also includes: a second determination unit, which determines whether the movement of the patient table exceeds a limit value of the allowed movement according to a relationship between a preset height of the patient table and an allowed movement range and a determined distance between a geometric center of the organ of interest and a geometric rotation center of the medical imaging device; The control unit drives the patient table to move in combination with the determination result of the second determination unit, so that the movement of the patient table does not exceed the limit value of the allowed movement.

15. The device according to claim 14, wherein: The relationship is a configuration file in a table format, and the configuration file includes a plurality of patient tables and an allowable movement range corresponding to the height of each patient table.

16. The device according to claim 14, wherein: The relationship is a fitted function of the patient table height and the allowed range of movement.

17. A medical imaging method, characterized in that: The method comprises: Obtaining a region of interest on a scout image of an inspection object using a deep learning method; Determining the geometric center of the organ of interest of the examination object according to the region of interest; The patient table is driven to move according to the geometric center of the organ of interest and the geometric rotation center of the medical imaging device.

18. The method according to claim 17, wherein: The method further comprises: A reconstruction center and a display field of view of a diagnostic image of the examination object are determined according to the organ of interest.

19. A medical imaging device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the medical imaging method according to claim 17 or 18.

Images