Calibration method and device of medical imaging equipment and medical imaging equipment

Abstract

The embodiment of the invention provides a medical imaging equipment calibration method and device and medical imaging equipment. The calibration method comprises the following steps that: the medical imaging equipment scans a motif to generate an image containing an image of the motif; analyzing whether the motif is abnormal or not based on the image; and in response to an analysis result that the motif is abnormal, prompting that the motif is abnormal. According to the embodiment of the invention, whether the motif is abnormal or not is detected, and when the motif is abnormal, the abnormality is prompted, so that the accuracy of a calibration or detection result can be ensured.

Description

Technical Field

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

[0002] Medical imaging equipment is used to scan patients in a non-invasive manner to obtain medical images of the patient's anatomical tissue of interest to assist doctors in diagnosis. For example, CT (Computed Tomography), which uses precisely collimated X-ray beams, gamma rays, etc., together with highly sensitive detectors to scan sections around a certain part of the human body one by one, has the characteristics of fast scanning time and clear images, and can be used to examine a variety of diseases.

[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] Medical imaging equipment such as CT equipment sometimes needs to be calibrated or tested before or during use. For example, the medical imaging equipment is scanned at a specified position of a standard phantom, and the scanning results are used to calibrate the medical imaging equipment or determine whether the medical imaging equipment meets the requirements.

[0007] The inventors have discovered that when scanning a phantom, the phantom may sometimes have some abnormalities, which may lead to abnormalities in the scanning result, thereby affecting the calibration or detection result.

[0008] In response to at least one of the above-mentioned technical problems or other similar problems, the embodiments of the present application provide a calibration method, device and medical imaging device for medical imaging equipment, which can prompt the abnormality when there is an abnormality in the model, thereby ensuring the accuracy of the calibration or detection results.

[0009] According to one aspect of an embodiment of the present application, a calibration method for a medical imaging device is provided, the calibration method comprising:

[0010] The medical imaging device scans the phantom to generate an image including an image of the phantom;

[0011] Analyzing whether the phantom has abnormalities based on the image; and

[0012] In response to the analysis result that the phantom is abnormal, it is prompted that the phantom is abnormal.

[0013] According to another aspect of an embodiment of the present application, a calibration device for a medical imaging device is provided, the calibration device comprising:

[0014] an image acquisition unit, which acquires an image including an image of the phantom generated by scanning the phantom with the medical imaging device;

[0015] an analyzing unit, which analyzes whether the phantom has an abnormality based on the image; and

[0016] A prompting unit is configured to prompt that the phantom has an abnormality in response to an analysis result that the phantom has an abnormality.

[0017] 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 calibration method of the medical imaging device as described above.

[0018] One of the beneficial effects of the embodiments of the present application is that the embodiments of the present application detect whether there is an abnormality in the model, and when there is an abnormality in the model, the abnormality is prompted, thereby ensuring the accuracy of the calibration or detection results and improving the efficiency of the calibration.

[0019] 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

[0020] 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:

[0021] Figure 1 is a schematic diagram of a CT device according to an embodiment of the present application;

[0022] Figure 2 is a schematic diagram of a CT imaging system according to an embodiment of the present application;

[0023] Figure 3 is a schematic diagram of a calibration method for a medical imaging device according to an embodiment of the present application;

[0024] Figure 4 is a schematic diagram of operation 301;

[0025] Figure 5 This is a schematic diagram of situation 1;

[0026] Figure 6 This is a schematic diagram of situation 2;

[0027] Figure 7 This is a schematic diagram of situation 3;

[0028] Figure 8 This is another schematic diagram of situation 3;

[0029] Fig. 9 This is a schematic diagram of situation 4;

[0030] Fig.10 It is a schematic diagram for analyzing whether the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device;

[0031] Fig.11 yes Fig.10 Schematic diagram corresponding to each operation;

[0032] Fig.12 is a schematic diagram of the neural network;

[0033] Fig.13 It is a schematic diagram for analyzing whether the placement posture of the phantom is abnormal;

[0034] Fig.14 yes Fig.13 Schematic diagram corresponding to each operation;

[0035] Fig.15 It is a schematic diagram of the first warning information;

[0036] Fig.16 It is a schematic diagram of the second warning information;

[0037] Fig.17 is a flow chart for testing a medical imaging device using the calibration method of the medical imaging device of the present application;

[0038] Fig.18 is a schematic diagram of a calibration device for a medical imaging device according to an embodiment of the present application;

[0039] Fig.19 It is a schematic diagram of a medical imaging device according to an embodiment of the present application. DETAILED DESCRIPTION

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] The medical imaging device described in the present application can be applicable to various medical imaging modalities, including but not limited to CT (computed tomography) imaging equipment, PET (positron emission tomography)-CT, magnetic resonance imaging (MRI) or any other suitable medical imaging equipment.

[0045] The system for obtaining medical imaging data may include the aforementioned medical imaging device, and may also include a separate computer device connected to the medical imaging device, and may also include a computer device connected to the Internet cloud, which is 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. For example, the system for obtaining medical imaging data may be a CT imaging system, etc.

[0046] Exemplarily, the embodiments of the present application are described below in conjunction with an X-ray computed tomography (CT) imaging device. Those skilled in the art will appreciate that the embodiments of the present application may also be applicable to other medical imaging devices.

[0047] Figure 1 FIG. 1 is a schematic diagram of a CT device according to an embodiment of the present application, schematically illustrating the CT device 100. Figure 1 As 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 toward a detector assembly or a collimator 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 scanning by the X-ray source 103, medical image data of the test object 105 can be obtained.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] The above schematically illustrates medical image scanning, and the following specifically describes the embodiments of the present application in conjunction with the accompanying drawings. In the following embodiments, the medical imaging device is a CT device as an example for description, and the description is also applicable to other medical imaging devices.

[0056] First aspect embodiment

[0057] An embodiment of the present application provides a calibration method for a medical imaging device.

[0058] Figure 3 FIG. 1 is a schematic diagram of a calibration method for a medical imaging device according to an embodiment of the present application. Figure 3 As shown, the calibration method includes the following operations:

[0059] Operation 301: A medical imaging device scans a phantom to generate an image including an image of the phantom;

[0060] Operation 302: Analyze whether the phantom has an abnormality based on the image; and

[0061] Operation 303 : In response to the analysis result that the phantom has an abnormality, prompting that the phantom has an abnormality.

[0062] The embodiment of the present application detects whether there is an abnormality in the phantom, and when there is an abnormality in the phantom, the abnormality is prompted, so that the operator can adjust or replace the phantom based on the prompt to obtain an appropriate image of the phantom.

[0063] The calibration method of the present application can be used to calibrate the parameters of a medical imaging device. For example, when an image of a suitable phantom is obtained, the parameters used by the medical imaging device are calibrated based on the image of the suitable phantom. In addition, the calibration method of the present application can also be used to test the medical imaging device. For example, when an image of a suitable phantom is obtained, it is determined based on the image of the suitable phantom whether the medical imaging device can perform normal operations such as imaging.

[0064] Figure 4 is a schematic diagram of operation 301. Figure 4 As shown, a phantom 400 is placed on the patient table 102 , and as the patient table 102 moves into the scanning gantry opening 106 , the X-ray source 103 projects an X-ray beam toward the detector assembly or collimator 104 on the opposite side of the scanning gantry 101 for scanning, thereby generating an image containing an image of the phantom 400 .

[0065] like Figure 4 As shown, the direction in which the patient table 102 moves into or out of the scanning gantry opening 106 is the Z direction, the X direction is the horizontal direction, and the Y direction is the vertical direction, wherein the X direction, the Y direction, and the Z direction are perpendicular to each other.

[0066] In this application, the motif has an abnormality, including at least one of the following situations:

[0067] Case 1: There is foreign matter inside the phantom;

[0068] Case 2: The center of the phantom is not aligned with the center of the scanning field of view of the medical imaging device;

[0069] Case 3: The placement of the phantom is abnormal and / or the type of the phantom is incorrect;

[0070] Case 4: The placement posture of the model is abnormal.

[0071] Figure 5 is a schematic diagram of situation 1. Figure 5 In the present invention, the foreign matter inside the phantom body may be, for example, air bubbles. Figure 5 As shown in (A), bubbles 501 appear in the area near the surface of the image 500 of the phantom. Figure 5 As shown in (B), bubbles 501 appear in the upper area of ​​the image 500 of the phantom. Figure 5 As shown in (C), bubbles 501 appear in the structure of the image 500 of the phantom.

[0072] Figure 6 is a schematic diagram of situation 2. Figure 6 As shown in (A), in image 601, the center of the image 600 of the phantom coincides with the center of image 601, so the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device. Figure 6 As shown in (B), in image 601, the center of the image 600 of the phantom is lower than the center of the image 601, so the center of the phantom is not aligned with the center of the scanning field of view of the medical imaging device. Figure 6As shown in (C), in the image 601, the center of the image 600 of the phantom is to the right relative to the center of the image 601, so the center of the phantom is not aligned with the center of the scanning field of view of the medical imaging device.

[0073] Figure 7 is a schematic diagram of situation 3. The structure and / or shape of the phantom 400 at different positions along the longitudinal axis may be different. Therefore, when the phantom 400 is placed at different positions in the Z direction of the scanning gantry 101, the image of the phantom in the image may be different. Figure 7 (A), (B), (C), and (D) respectively show images 701 of the same phantom when the phantom is located at different positions in the Z direction of the scanning gantry 101 .

[0074] Figure 8 is another schematic diagram of situation 3. Different types of phantoms 400 may have different structures and / or shapes. Therefore, when different types of phantoms 400 are placed on the scanning gantry 101, the images of the phantoms in the images may be different. Figure 8 (A), (B), and (C) respectively show images 801 of different types of phantoms.

[0075] Fig. 9 is a schematic diagram of situation 4. In the present application, the abnormal placement posture of the phantom may be, for example, that the length direction of the phantom 400 is not parallel to the Z direction of the scanning gantry 101 (or the angle between the length direction of the phantom 400 and the Z direction of the scanning gantry 101 is not 0), that is, the phantom 400 is tilted relative to the Z direction of the scanning gantry 101, so that the image of the phantom 400 becomes an ellipse. If the length direction of the phantom 400 is parallel to the Z direction of the scanning gantry 101, the image of the phantom 400 is a circle. Fig. 9 (A) shows the motif in Figure 4 An image 901 of the phantom when it is tilted relative to the Z direction on the YZ plane. Fig. 9 (B) shows the motif in Figure 4 An image 901 of the phantom when it is tilted relative to the Z direction on the XZ plane.

[0076] In operation 302 of the present application, analyzing whether the phantom has an abnormality includes:

[0077] Analyze whether there is foreign matter inside the phantom, corresponding to the above-mentioned situation 1;

[0078] Analyzing whether the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device, corresponding to the above-mentioned situation 2;

[0079] Analyze whether the placement position of the phantom is abnormal and / or whether the type of the phantom is correct, corresponding to the above-mentioned situation 3;

[0080] Analyze whether the placement posture of the model is abnormal, which corresponds to the above-mentioned situation 4.

[0081] In operation 302 of the present application, for the above-mentioned situation 1, when analyzing whether there is a foreign object inside the phantom, the image obtained in operation 301 can be analyzed based on the trained first neural network to determine whether there is a foreign object inside the phantom. For example, by using images of foreign objects of different positions, shapes, and sizes inside the phantom as training images, the neural network is trained to obtain the trained first neural network.

[0082] In operation 302 of the present application, for the above-mentioned situation 2, analyzing whether the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device may include: Fig.10 The following operations are shown:

[0083] Operation 1001: identifying an image of the phantom from the image;

[0084] Operation 1002: Calculate the distance from the edge of the image of the phantom to the The center point of the image For example, the image edge of the phantom may have four points, namely, a point on the upper edge, a point on the lower edge, a point on the left edge, and a point on the right edge, etc. In addition, the present application is not limited thereto, and the edge points may have other numbers and other positions; and

[0085] Operation 1003 : determining whether the image of the phantom is located at the center of the image according to the relationship between the distance and the threshold.

[0086] Fig.11 yes Fig.10 Schematic diagram corresponding to each operation in .

[0087] Fig.11 (A) shows an image 1100;

[0088] Fig.11 (B) shows an image 1101 of a phantom identified from the image 1100;

[0089] Fig.11 (C) shows edge points extracted from the image 1101 of the model, where the number of edge points is, for example, 4, namely, point A of the upper edge, point B of the lower edge, point C of the left edge, and point D of the right edge, etc. The coordinates of the edge points are point A (256, 36), point B (255.5, 476), point C (35, 256.5), and point D (476, 257), respectively. The coordinates of the center point O of the image 1100 are (255.5, 256), and the height of the image 1100 is 440, and the length is 441;

[0090] like Fig.11 As shown in (D), the distances from the edge points A, B, C, and D to the center point O of the image 1100 are L1, L2, L3, and L4, respectively.

[0091] In operation 1003, if the absolute value of the difference in distances from the left edge point C and the right edge point D to the center point O is greater than a first threshold, it is determined that the center of the model is not aligned with the center of the scanning field of view of the medical imaging device in the left-right direction; if the absolute value of the difference in distances from the upper edge point A and the lower edge point B to the center point O is greater than a second threshold, it is determined that the center of the model is not aligned with the center of the scanning field of view of the medical imaging device in the upper-lower direction.

[0092] For example, the first threshold is 3, the second threshold is 3, such as Fig.11 As shown in (D), L3 is 220, L4 is 221, and the difference between L3 and L4 is 1, which is less than the first threshold value. Therefore, it is judged that the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device in the left-right direction; L1 is 220, L2 is 220, and the difference between L1 and L2 is 0, which is less than the second threshold value. Therefore, it is judged that the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device in the up-down direction.

[0093] In operation 302 of the present application, for the above-mentioned situation 3, when analyzing whether the placement position of the phantom is abnormal and / or analyzing whether the type of the phantom is correct, the image obtained in operation 301 can be analyzed based on the trained second neural network to determine whether the placement position of the phantom is abnormal and / or analyze whether the type of the phantom is correct. For example, by using images of phantoms placed in different positions and of different types as training images, the neural network is trained to obtain the trained second neural network.

[0094] In the above embodiment, there is no limitation on the specific structures of the trained first neural network and the trained second neural network. For example, a convolutional neural network model, a deep trust network model, a stacked autoencoder network model, etc. may be used. A typical example is a trained U-net. For details, please refer to the relevant technology, and the description is omitted here.

[0095] In some embodiments of the present application, the trained first neural network and the trained second neural network may be the same neural network, that is, one neural network may simultaneously perform analysis on situation 1 and situation 3, that is, when an image is input into the neural network, the neural network may output analysis results on whether there are foreign objects inside the model, whether the placement position of the model is abnormal, and / or whether the type of the model is correct.

[0096] Fig.12 is a schematic diagram of the neural network. Fig.12 As shown, the neural network can extract features in the image through a feature extraction layer, and then detect the extracted features through a detection unit to obtain an analysis result. The neural network can include, for example, multiple convolutional layers (Conv) and other processing units.

[0097] In addition, in some other embodiments of the present application, the trained first neural network and the trained second neural network may be two independent neural networks, and the structures of the two independent neural networks may be the same as Fig.12 Similarly, other structures are also possible.

[0098] In operation 302 of the present application, for the above-mentioned situation 4, analyzing whether the placement posture of the phantom is abnormal may include: Fig.13 The following operations are shown:

[0099] Operation 1301, identifying an image of the phantom from the image;

[0100] Operation 1302, calculating the position of the center point of the image of the phantom;

[0101] Operation 1303: Calculate the distance from the edge of the image of the phantom to the image of the phantom. Center Point For example, the image edge of the phantom may have four points, namely, a point on the upper edge, a point on the lower edge, a point on the left edge, and a point on the right edge, etc. In addition, the present application is not limited thereto, and the edge points may have other numbers and other positions; and

[0102] Operation 1304 : determining whether the placement posture of the phantom is abnormal based on the relationship between the distance and the threshold.

[0103] Fig.14 yes Fig.13 Schematic diagram corresponding to each operation in .

[0104] Fig.14 (A) shows an image 1400;

[0105] Fig.14 (B) shows an image 1401 of a phantom identified from the image 1400;

[0106] Fig.14(C) shows edge points extracted from the image 1101 of the phantom, where the number of edge points is, for example, 4, namely, point A of the upper edge, point B of the lower edge, point C of the left edge, and point D of the right edge, etc. The coordinates of the edge points are point A (256, 54), point B (255.5, 456), point C (35, 238), and point D (476, 238.5), respectively, and the coordinates of the center point O1 of the image 1401 of the phantom are (255.5, 255);

[0107] like Fig.14 As shown in (D), the distances from the edge points A, B, C, and D to the center point O1 of the image 1401 of the phantom are L1, L2, L3, and L4 respectively.

[0108] In operation 1304 , if the absolute value of the difference between L1 (or L2 ) and L3 (or L4 ) is greater than the third threshold, it is determined that the image 1401 of the phantom is not circular, that is, the placement posture of the phantom is abnormal.

[0109] For example, the third threshold is 5, such as Fig.14 As shown in (D), L1 is 201, L2 is 201, L3 is 220, and L4 is 221; the difference between L1 and L3 is 19, and the difference between L2 and L4 is 20, both of which are greater than the third threshold value of 5. Therefore, it is judged that the placement posture of the model is abnormal.

[0110] In operation 303 of the present application, in response to the analysis result that the motif has an abnormality (for example, the analysis result of operation 302 is that at least one of situation 1, situation 2, situation 3 and situation 4 has occurred), it can be indicated that the motif has an abnormality.

[0111] Among them, at least one of the following forms can be used to indicate that the model is abnormal:

[0112] Form 1: The display of the medical imaging device displays the first warning information, wherein the first warning information can be displayed in the form of a dialog box, through text or pictures, for example, Fig.15 It is a schematic diagram of the first warning information, such as Fig.15 As shown, the dialog box 1500 may display the text “bubbles detected in the model” and the like;

[0113] Form 2: Display the second warning information in the area of ​​the image corresponding to the abnormality, wherein the second warning information can be displayed by text or pictures, for example, Fig.16 is a schematic diagram of the second warning information, such as Fig.16As shown, in image 1600, a bounding box 1601 is displayed in the area corresponding to the abnormality to locate the abnormality, and a second warning message is provided near the bounding box 1601 through text, which can be used to indicate the type of abnormality, such as "bubble", "position abnormality", etc.

[0114] Fig.17 FIG. 1 is a flow chart of testing a medical imaging device using the calibration method of the medical imaging device of the present application. Fig.17 As shown, the flow chart includes:

[0115] Operation 1701: placing a phantom in a medical imaging device. For example, an operator may place the phantom 400 on a patient table 102.

[0116] Operation 1702: selecting a test procedure, for example, through an operation panel of a medical imaging device, etc.;

[0117] Operation 1703: The medical imaging device runs a test program. For example, the medical imaging device scans the phantom to generate an image including an image of the phantom.

[0118] Operation 1704: Analyze whether the phantom has an abnormality based on the image obtained in operation 1703. If the phantom has an abnormality, it is indicated that the phantom has an abnormality, and the process can return to operation 1701, and the operator can adjust the phantom appropriately. In addition, if the phantom has no abnormality, operation 1705 is performed.

[0119] Operation 1705: analyzing relevant parameters of the medical imaging device based on the image obtained in operation 1703, that is, testing the medical imaging device;

[0120] Operation 1706: Output a test report.

[0121] 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.

[0122] According to the embodiment of the first aspect of the present application, a test is performed to determine whether the phantom has an abnormality, and when the phantom has an abnormality, the abnormality is prompted, so that the operator can make timely adjustments to the phantom, thereby ensuring the accuracy of the calibration or test results. For example, during the calibration process, the operator can be reminded of the abnormality of the phantom in a timely manner, thereby preventing erroneous calibration results or test results; in addition, for inexperienced operators, they can know in a timely manner the problems that occurred before or during the calibration or test, and know how to adjust, thereby helping to eliminate the abnormality of the phantom.

[0123] In addition, in the present application, common abnormalities of the phantom are classified, and different types of phantom abnormalities are prompted (for example, the above-mentioned cases 1 to 4), so that the operator can adjust the phantom in a targeted manner, reducing the difficulty of adjustment.

[0124] In addition, in the present application, corresponding detection methods are set for different types of model abnormalities. For example, for cases 1 and 3, neural networks are used for detection, and for cases 2 and 4, points on the edge of the model's image are used for detection. In this way, different types of model abnormalities can be effectively detected, thereby improving the accuracy and efficiency of detection.

[0125] Embodiments of the second aspect

[0126] An embodiment of the second aspect of the present application provides a calibration device for a medical imaging device, and the contents that are the same as those of the embodiment of the first aspect are not repeated here.

[0127] Fig.18 FIG. 1 is a schematic diagram of a calibration device for a medical imaging device according to an embodiment of the present application. Fig.18 As shown, the calibration device 1800 of the medical imaging device of the embodiment of the present application includes:

[0128] An image acquisition unit 1801 is configured to obtain an image including an image of the phantom generated by scanning the phantom with the medical imaging device;

[0129] An analyzing unit 1802 is configured to analyze whether the phantom has an abnormality based on the image; and

[0130] The prompting unit 1803 is configured to prompt that the phantom has an abnormality in response to the analysis result that the phantom has an abnormality.

[0131] In some embodiments, the analyzing unit 1802 analyzes whether the phantom has an abnormality, including:

[0132] Analyzing whether there is foreign matter inside the phantom; and / or

[0133] Analyzing whether the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device; and / or

[0134] Analyzing whether the placement position of the phantom is abnormal; and / or

[0135] Analyzing whether the type of the phantom is correct; and / or

[0136] Analyze whether the placement posture of the phantom is abnormal.

[0137] In some embodiments, the analyzing unit 1802 analyzes whether there is foreign matter inside the phantom, including:

[0138] The image is analyzed based on the trained first neural network to determine whether there is a foreign object inside the phantom.

[0139] In some embodiments, the analyzing unit 1802 analyzes whether the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device, including:

[0140] identifying an image of the phantom from the image;

[0141] Calculate the distance from the edge of the image of the phantom to the The center point of the image distance;

[0142] According to the relationship between the distance and the threshold, it is determined whether the image of the phantom is located at the center of the image.

[0143] In some embodiments, the analyzing unit 1802 analyzes whether the placement position of the phantom is abnormal and / or analyzes whether the type of the phantom is correct, including:

[0144] The image is analyzed based on the trained second neural network to determine whether the placement position of the phantom in the medical imaging device is abnormal and / or whether the type of the phantom is correct.

[0145] In some embodiments, the analyzing unit 1802 analyzes whether the placement posture of the phantom is abnormal, including:

[0146] identifying an image of the phantom from the image;

[0147] Calculating the position of the center point of the image of the phantom;

[0148] Calculate the distance from the edge of the image of the phantom to the The center point of the image distance; and

[0149] Whether the placement posture of the phantom is abnormal is determined according to the relationship between the distance and the threshold.

[0150] In some embodiments, the prompting unit 1803 prompts that the phantom has an abnormality, including:

[0151] causing a display of the medical imaging device to display first warning information; and / or

[0152] The second warning information is displayed in the area of ​​the image corresponding to the abnormality.

[0153] To keep it simple, Fig.18 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.

[0154] 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.

[0155] According to an embodiment of the second aspect of the present application, a detection is performed to determine whether there is any abnormality in the phantom. When an abnormality is found in the phantom, the abnormality is prompted, so that the operator can make timely adjustments to the phantom, thereby ensuring the accuracy of the calibration or detection results.

[0156] Embodiments of the third aspect

[0157] The present application embodiment provides a medical imaging device, including the calibration device 1800 as described in the embodiment of the second aspect, the contents of which are incorporated herein. The medical imaging device may have, for example, a computer, a server, a workstation, a laptop computer, a smart phone, etc., but the present application embodiment is not limited thereto.

[0158] Fig.19 Schematic diagram of a medical imaging device according to an embodiment of the present application. Fig.19 As shown, the medical imaging device 1900 may include: one or more processors (e.g., central processing unit CPU) 1910 and one or more memories 1920; the memory 1920 is coupled to the processor 1910. The memory 1920 may store various data; in addition, it may store a program 1921 for information processing, and execute the program 1921 under the control of the processor 1910.

[0159] In some embodiments, the functions of the calibration device 1800 are integrated into the processor 1910. The processor 1910 is configured to implement the calibration method as described in the embodiment of the first aspect.

[0160] In some embodiments, the calibration device 1800 is configured separately from the processor 1910 . For example, the calibration device 1800 can be configured as a chip connected to the processor 1910 , and the functions of the calibration device 1800 are implemented through the control of the processor 1910 .

[0161] For example, the processor 1910 is configured to perform the following control: it obtains an image including an image of the phantom generated by the medical imaging device scanning the phantom; analyzes whether there is an abnormality in the phantom based on the image; and in response to the analysis result that there is an abnormality in the phantom, prompts that there is an abnormality in the phantom.

[0162] In addition, if Fig.19 As shown, the medical imaging device 1900 may also include: an input / output (I / O) device 1930 and a display 1940, 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 1900 does not necessarily include Fig.19 In addition, the medical imaging device 1900 may also include Fig.19 For components not shown in the figure, reference may be made to the related art.

[0163] 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 calibration method of a medical imaging device as described in the embodiment of the first aspect in the electronic device.

[0164] 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 calibration method of a medical imaging device as described in the embodiment of the first aspect in an electronic device.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

Claims

1. A method for calibrating a medical imaging device, characterized in that: The calibration method comprises: The medical imaging device scans the phantom to generate an image including an image of the phantom; Analyzing whether the phantom has abnormalities based on the image; and In response to the analysis result that the phantom is abnormal, it is prompted that the phantom is abnormal.

2. The calibration method according to claim 1, characterized in that: Analyzing whether the model has abnormalities, including: Analyzing whether there is foreign matter inside the phantom; and / or Analyzing whether the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device; and / or Analyzing whether the placement position of the phantom is abnormal; and / or Analyzing whether the type of the phantom is correct; and / or Analyze whether the placement posture of the phantom is abnormal.

3. The calibration method according to claim 2, characterized in that: Analyzing whether there is foreign matter inside the phantom, including: The image is analyzed based on the trained first neural network to determine whether there is a foreign object inside the phantom.

4. The calibration method according to claim 2, characterized in that: Analyzing whether the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device includes: identifying an image of the phantom from the image; Calculate the distance from the edge of the image of the phantom to the The center point of the image distance; According to the relationship between the distance and the threshold, it is determined whether the image of the phantom is located at the center of the image.

5. The calibration method according to claim 2, characterized in that: Analyzing whether the placement position of the phantom is abnormal and / or analyzing whether the type of the phantom is correct includes: The image is analyzed based on the trained second neural network to determine whether the placement position of the phantom in the medical imaging device is abnormal and / or whether the type of the phantom is correct.

6. The calibration method according to claim 2, characterized in that: Analyze whether the placement posture of the phantom is abnormal, including: identifying an image of the phantom from the image; Calculating the position of the center point of the image of the phantom; Calculate the distance from the edge of the image of the phantom to the The center point of the image distance; and Whether the placement posture of the phantom is abnormal is determined according to the relationship between the distance and the threshold.

7. The calibration method according to claim 1, characterized in that: It indicates that the model has abnormalities, including: causing a display of the medical imaging device to display first warning information; and / or The second warning information is displayed in the area of ​​the image corresponding to the abnormality.

8. A calibration device for medical imaging equipment, characterized in that: The calibration device comprises: an image acquisition unit, which acquires an image including an image of the phantom generated by scanning the phantom with the medical imaging device; an analyzing unit, which analyzes whether the phantom has an abnormality based on the image; and A prompting unit is configured to prompt that the phantom has an abnormality in response to an analysis result indicating that the phantom has an abnormality.

9. The calibration device according to claim 8, characterized in that The analyzing unit analyzes whether the model has an abnormality, including: Analyzing whether there is foreign matter inside the phantom; and / or Analyzing whether the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device; and / or Analyzing whether the placement position of the phantom is abnormal; and / or Analyzing whether the type of the phantom is correct; and / or Analyze whether the placement posture of the phantom is abnormal.

10. The calibration device according to claim 9, characterized in that The analyzing unit analyzes whether there is foreign matter inside the phantom, including: The image is analyzed based on the trained first neural network to determine whether there is a foreign object inside the phantom.

11. The calibration device according to claim 9, characterized in that The analyzing unit analyzes whether the center of the phantom is aligned with the center of the scanning field of view of the medical imaging device, including: identifying an image of the phantom from the image; Calculate the distance from the edge of the image of the phantom to the The center point of the image distance; According to the relationship between the distance and the threshold, it is determined whether the image of the phantom is located at the center of the image.

12. The calibration device according to claim 9, characterized in that The analyzing unit analyzes whether the placement position of the phantom is abnormal and / or analyzes whether the type of the phantom is correct, including: The image is analyzed based on the trained second neural network to determine whether the placement position of the phantom in the medical imaging device is abnormal and / or whether the type of the phantom is correct.

13. The calibration device according to claim 9, characterized in that The analyzing unit analyzes whether the placement posture of the phantom is abnormal, including: identifying an image of the phantom from the image; Calculating the position of the center point of the image of the phantom; Calculate the distance from the edge of the image of the phantom to the The center point of the image distance; and Whether the placement posture of the phantom is abnormal is determined according to the relationship between the distance and the threshold.

14. The calibration device according to claim 8, characterized in that The prompting unit prompts that the phantom has an abnormality, including: causing a display of the medical imaging device to display first warning information; and / or The second warning information is displayed in the area of ​​the image corresponding to the abnormality. 15 . 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 calibration method according to claim 1 .

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