A method and system for automatic planning of scans

Abstract

The embodiment of the specification provides a method for automatically planning scanning, which comprises determining a first position of a patient bed based on a positioning image of a scanning object; determining a second position based on preset parameters and the positioning image, wherein the preset parameters comprise at least one of a medicine injected into the scanning object and a target region of interest of the scanning object, and the second position is related to the target region of interest; generating a scanning scheme based on the second position, wherein the scanning scheme comprises at least one of a moving position of the patient bed and a time corresponding to the moving position, and the scanning scheme is related to a flow rate of the medicine in the scanning object; and controlling a medical imaging device to scan the target region of interest from the second position based on the scanning scheme.

Description

[0001] illustrate

[0002] This application is a divisional application of Chinese application filed on December 31, 2021, with application number 202111678287.X and entitled "A method and system for automatic planning and scanning". Technical Field

[0003] This manual relates to the field of medical imaging, and in particular to a method and system for automated scanning. Background Technology

[0004] In medical scanning, dynamic parameters are frequently used in the scanning process. In such cases, the patient bed needs continuous movement to precisely position itself at the target organ at the appropriate time for scanning. Therefore, it is crucial to ensure that data acquisition begins simultaneously with drug injection, and that information about blood pool areas such as the descending / ascending aorta is detected after drug injection. Furthermore, depending on the drug, the organs requiring assessment must be placed at the center of the scanning field of view. These tasks are typically performed by clinicians based on experience, manually planning the scanning range, resulting in a heavy workload and inconsistent accuracy. Moreover, to ensure complete capture of drug entry information, scanning is usually initiated before drug injection, incurring a period of ineffective scanning time, which introduces errors into subsequent qualitative and quantitative data analysis.

[0005] Therefore, it is desirable to provide a method and system for automatically planning scans. Summary of the Invention

[0006] One embodiment of this specification provides a method for automatically planning a scan. The method includes: determining a first position of a hospital bed based on a positioning image of the object to be scanned; determining a scanning plan based on preset parameters, the scanning plan including at least one of a moving position of the hospital bed and a time corresponding to the moving position; and, based on the scanning plan, moving the hospital bed to the first position and scanning the object using a medical imaging device.

[0007] One embodiment of this specification provides an automatic scanning planning system, including a positioning module, a planning module, and an acquisition module; the positioning module is used to determine a first position of a hospital bed based on a positioning image of the object to be scanned; the planning module is used to determine a scanning plan based on preset parameters, the scanning plan including at least one of the moving position of the hospital bed and the time corresponding to the moving position; the acquisition module is used to move the hospital bed to the first position based on the scanning plan and scan the object using medical imaging equipment.

[0008] One embodiment of this specification provides an apparatus for automatically planning a scan, including a processor for performing the method of automatically planning a scan.

[0009] One embodiment of this specification provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the automatic planning scan method. Attached Figure Description

[0010] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0011] Figure 1 These are schematic diagrams illustrating application scenarios of an automated scanning system based on some embodiments of this specification;

[0012] Figure 2 This is a schematic diagram of an automated scanning system according to some embodiments of this specification;

[0013] Figure 3 This is an exemplary flowchart of an automatic scanning planning method according to some embodiments of this specification;

[0014] Figure 4 This is a schematic diagram of an automatic scanning method according to some embodiments of this specification;

[0015] Figure 5A , Figure 5B This is a schematic diagram illustrating the automatic identification of the aortic blood pool area and the automatic bed transfer to the corresponding position according to some embodiments of this specification;

[0016] Figure 6A , Figure 6B This is a schematic diagram illustrating the automatic identification of a patient's major organs and the automatic planning of a scanning scheme according to some embodiments of this specification. Detailed Implementation

[0017] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0018] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0019] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0020] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0021] In some application scenarios, an automated scanning system may include processing equipment and medical imaging equipment. The automated scanning system can implement the methods and / or processes disclosed in this specification through processing equipment to realize automated scanning plan planning. Based on the scanning plan, the medical imaging equipment realizes image acquisition, thereby realizing the automated planning and execution of the scanning plan in dynamic parameter scanning, reducing the workload of clinicians, improving the accuracy of scanning planning, thereby accurately and effectively acquiring the patient's medical images, and reducing the adverse effects on the patient's body.

[0022] like Figure 1 As shown, in some embodiments, system 100 may include medical imaging equipment 110, processing equipment 120, storage device 130, terminal 140, and network 150.

[0023] Medical imaging equipment 110 refers to a medical device that uses different media to reproduce images of the internal structures of the human body. In some embodiments, medical imaging equipment 110 can be any medical device capable of imaging or treating designated body parts of a patient, such as DR (Digital Radiography), CT (Computed Tomography), PET (Positron Emission Tomography), PET-CT, SPECT-CT (Single-Photon Emission Computed Tomography), etc. The medical imaging equipment 110 described above is for illustrative purposes only and is not intended to limit its scope. In some embodiments, medical imaging equipment may include a scanning device and a hospital bed, with the patient fixed on the bed during imaging, and the scanning device scanning the patient. In some embodiments, the relative position of the hospital bed and the scanning device can be changed by moving the hospital bed and / or the scanning device, thereby positioning the patient in a predetermined scanning position. In some embodiments, the hospital bed and the medical imaging equipment may be two separate devices.

[0024] In some embodiments, the medical imaging device 110 can acquire medical images of the patient, such as localization images, plain scan images, and enhanced scan images, and send them to the processing device 120. In some embodiments, at least some parameters of the medical imaging device 110 can be stored in the storage device 130. These parameters may be related to the scanning protocol, such as scanning range, scanning time, bed overlap, injected drugs, and target region of interest. The medical imaging device 110 can receive instructions sent by the processing device 120 or the terminal 140 and perform related operations according to the instructions, such as moving the bed to a designated location or acquiring medical images of the patient according to the scanning protocol. In some embodiments, the medical imaging device 110 can exchange data and / or information with other components in the system 100 (e.g., the processing device 120, the storage device 130, and the terminal 140) through the network 150. In some embodiments, the medical imaging device 110 can be directly connected to other components in the system 100. In some embodiments, one or more components in the system 100 (e.g., the processing device 120 and the storage device 130) may be included within the medical imaging device 110.

[0025] The processing device 120 can process data and / or information obtained from other devices or system components, and execute the automatic scanning planning method shown in some embodiments of this specification based on this data, information, and / or processing results to perform one or more functions described in some embodiments of this specification. For example, the processing device 120 can identify specific organs and / or body tissues of a patient based on medical images from the medical imaging device 110 and obtain their locations. As another example, the processing device 120 can automatically generate a scanning plan based on the identified organ / tissue locations and preset parameters. In some embodiments, the processing device 120 can send the processed data, such as scan range, scan time, bed overlap, etc., to the storage device 130 for storage. In some embodiments, the processing device 120 can retrieve pre-stored data and / or information from the storage device 130, such as location images, injected drugs, target regions of interest, etc., to execute the automatic scanning planning method shown in some embodiments of this specification, such as automatically generating a scanning plan.

[0026] In some embodiments, the processing device 120 may include one or more sub-processing devices (e.g., a single-core processing device or a multi-core multi-chip processing device). By way of example only, the processing device 120 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction processor (ASIP), a graphics processing unit (GPU), a physical processor (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, or any combination thereof.

[0027] Storage device 130 can store data or information generated by other devices. In some embodiments, storage device 130 can store data and / or information acquired by medical imaging device 110, such as localization images, plain scan images, enhanced scan images, etc. In some embodiments, storage device 130 can store data and / or information processed by processing device 120, such as scan range, scan time, bed overlap, etc. Storage device 130 may include one or more storage components, each of which may be a separate device or part of other devices. Storage device can be local or implemented via the cloud.

[0028] Terminal 140 can control the operation of medical imaging equipment 110. Doctors can issue operation commands to medical imaging equipment 110 through terminal 140 to enable it to perform specified operations, such as acquiring medical images or acquiring positioning images based on a predetermined scanning plan. In some embodiments, terminal 140 can instruct processing device 120 to execute an automatic scanning planning method as shown in some embodiments of this specification. In some embodiments, terminal 140 can receive a planned scanning plan from processing device 120 and determine the final scanning plan based on this plan. In some embodiments, terminal 140 can be one or any combination of mobile device 140-1, tablet computer 140-2, laptop computer 140-3, desktop computer, or other devices with input and / or output functions.

[0029] Network 150 can connect the various components of the system and / or connect the system to external resources. Network 150 enables communication between the components and with other parts outside the system, facilitating the exchange of data and / or information. In some embodiments, one or more components in system 100 (e.g., medical imaging device 110, processing device 120, storage device 130, terminal 140) can send data and / or information to other components via network 150. In some embodiments, network 150 can be any one or more of a wired network or a wireless network.

[0030] In some embodiments, system 100 may further include an operator ( Figure 1 (Not shown). An operator may perform drug injection and / or image acquisition operations, and may perform one or more operations. In some embodiments, the same operator may perform both drug injection and image acquisition operations. In some embodiments, different operators may perform both operations. In some embodiments, the operator may initiate image acquisition operations simultaneously with drug injection.

[0031] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this specification. Various changes and modifications can be made by those skilled in the art based on the content of this specification. Features, structures, methods, and other features of the exemplary embodiments described herein can be combined in various ways to obtain other and / or alternative exemplary embodiments. For example, the processing device 120 may be based on a cloud computing platform, such as a public cloud, private cloud, community cloud, and hybrid cloud. However, these changes and modifications will not depart from the scope of this specification.

[0032] Figure 2 This is a schematic diagram of an automated scanning system according to some embodiments of this specification.

[0033] like Figure 2 As shown, in some embodiments, the automatic scanning planning system 200 may include a positioning module 210, a planning module 220, and a data acquisition module 230.

[0034] In some embodiments, the positioning module 210 can be used to determine a first position of the bed based on a positioning image of the scanned object, i.e., the patient. The positioning module 210 can obtain the position of a specific organ / tissue of the scanned object from the positioning image, and then determine a first position based on the position of the specific organ / tissue, such that the specific organ / tissue is located at the center of the scanning field of view of the medical imaging device.

[0035] In some embodiments, the first location may correspond to a first region of interest. The positioning module 210 may include a first region acquisition unit 211 and a first location determination unit 212. The first region acquisition unit 211 may be used to acquire the first region of interest of the scanned object based on the positioning image using a machine learning model. The first location determination unit 212 may be used to determine the first location based on the first region of interest. In some embodiments, the first region of interest may be a blood pool, for example, the aortic blood pool.

[0036] In some embodiments, the scheme module 220 can be used to determine a scanning scheme based on preset parameters, wherein the scanning scheme may include at least one of the movement position of the bed and the time corresponding to the movement position. In some embodiments, the scheme module 220 can plan a scanning scheme for the target region of interest based on multiple preset parameters, such as the drug injected into the scanned object, the target region of interest of the scanned object, etc. In some embodiments, these scanning schemes may be scanning schemes that require drug injection.

[0037] In some embodiments, the scheme module 220 may include a second region acquisition unit 221, a second position determination unit 222, and a scheme determination unit 223. The second region acquisition unit 221 may be used to acquire one or more second regions of interest of the scanned object through a machine learning model based on preset parameters and positioning images. The second regions of interest may include a target region of interest. The second position determination unit 222 may be used to determine a second position based on at least one second region of interest. The scheme determination unit 223 may be used to determine a scanning scheme based on the second position, so that the bed moves from a first position to a second position to scan the target region of interest. The moving position of the bed may include the second position.

[0038] In some embodiments, there may be multiple second positions, and the scanning scheme may include determining the scanning overlap area corresponding to two adjacent second positions of the bed based on the multiple second positions.

[0039] In some embodiments, when the bed is in a second position, the target region of interest may be located at the center of the field of view (FOV) of the medical imaging device.

[0040] In some embodiments, the scheme determination unit 223 can also be used to determine a first time when the drug flows through the target region of interest based on the drug flow rate in the scanned object; determine a second time when the bed moves to a second position based on the first time; and generate a scanning scheme based on the second time, wherein the time corresponding to the moving position may include the second time. In some embodiments, the scanning scheme can be automatically generated by an algorithm or model (e.g., a machine learning model). In some embodiments, the generated scanning scheme can be modified manually.

[0041] In some embodiments, the acquisition module 230 can be used to move the hospital bed to a first position based on a scanning scheme, and then scan the object using medical imaging equipment. The acquisition module 230 can scan the target region of interest starting from the first position.

[0042] In some embodiments, the acquisition module 230 can be used to activate the medical imaging device to scan the object while injecting drugs into the object, using a preset method. The preset method may include at least one of the following: foot pedal activation, gesture recognition activation, voice recognition activation, EEG recognition activation, VR / AR device activation, device accessory activation, etc.

[0043] Figure 3 This is an exemplary flowchart of an automatic scanning method according to some embodiments of this specification.

[0044] like Figure 3 As shown, process 300 includes the following steps. In some embodiments, process 300 may be executed by processing device 120.

[0045] Step 310: Determine the first position of the hospital bed based on the positioning image of the scanned object. In some embodiments, step 310 may be performed by the positioning module 210.

[0046] Positioning images are medical images used to determine the relative position of a patient and a scanning device, such as DR images, plain CT images, etc. In some embodiments, positioning images can be acquired from various means, such as DR scans, plain CT scans, or from storage devices. In some embodiments, the location of specific organs / tissues can be identified from the positioning images, and the relative position of the patient and the scanning device can be obtained based on the identified specific organ / tissue locations, wherein these specific organs / tissues can be regions of interest in the scan / treatment.

[0047] The first region of interest (ROI) is an organ / tissue, etc., corresponding to a specific scan target. For example, in a scan (e.g., a PET scan), the first organ could be the aortic blood pool, etc. In some embodiments, the first ROI in the localization image can be identified by one or more methods (e.g., machine learning models, etc.). In some embodiments, the first ROI may correspond to the initial position of the scan.

[0048] The first position is the relative position of the bed and the scanning device associated with the first region of interest (ROI). This position ensures that the ROI of the scanned object (e.g., a patient) is located in the optimal scanning position, such as the center of the scanning field of view of the scanning device. In some embodiments, the first position may correspond to the first ROI. The relative position of the bed and the scanning device can be changed by means of moving the bed, etc., so that the ROI of the scanned object is located in the center of the scanning field of view of the scanning device.

[0049] In some embodiments, the relative position of the patient and the scanning device can be determined based on the positioning image, so that the bed can be moved to a designated position, that is, the relative position of the bed and the scanning device can be changed by the relative movement of the bed and the scanning device.

[0050] In some embodiments, the relative movement between the hospital bed and the scanning device can be referred to as bed moving, and the bed moving operation can be achieved by the movement of at least one of the hospital bed and the scanning device. In some embodiments, the bed moving can be automatically executed by a preset program or by automatically issuing commands, thereby causing the hospital bed to automatically move to a first position.

[0051] In some embodiments, the first region acquisition unit 211 can input the localization image into a machine learning model, which will automatically identify the first region of interest of the scanned object. In some embodiments, the machine learning model can be various models capable of extracting features from images, such as convolutional neural networks (CNNs).

[0052] In some embodiments, in order to track the location and movement of a drug in the human body during a scan (e.g., a PET scan), the first region of interest may be a blood pool region, such as the aortic blood pool.

[0053] The scanning location of a specific organ / tissue can be determined based on a localization image. In some embodiments, the first location determination unit 212 can determine a first location based on a first region of interest. After acquiring the first region of interest, the relative position of the patient and the scanning device is determined based on the location of the first region of interest in the human body, thereby obtaining the first location, which is the preset scanning location of the patient's first region of interest, such as the center of the scanning field of view of the scanning device.

[0054] Step 320: Determine a scanning scheme based on preset parameters. In some embodiments, step 320 may be performed by the scheme module 220.

[0055] Preset parameters are scan-related information specified before the scan begins, such as the injected medication, the target region of interest, and the scan start time. In some embodiments, preset parameters can be set by the user (e.g., an operator) before the scan according to the scan purpose. In some embodiments, there can be multiple preset parameters, including the medication injected into the scanned object and the target region of interest. The injected medication refers to the medication injected into the patient during the scan (e.g., a PET scan), and different medications may be targeted at different scan purposes. The target region of interest refers to the human organ related to the scan purpose, such as the liver, lungs, or stomach suspected of having cancer.

[0056] A scanning protocol refers to the information used to perform a scan, such as the scan range, scan time, and bed overlap. The scan range, also known as the scan area, refers to the area covered by the scan, such as the entire body or a portion of the body being scanned. Scan time refers to the time it takes for the bed to reach a specific scan position and / or the time it remains at that position; for example, the scan start time, scan duration, and scan end time. Scan time is related to the relative speed of movement between the bed and the scanning equipment.

[0057] Bed overlap refers to the overlapping area between two adjacent scanning positions. To scan multiple regions of interest of an object, multiple scanning positions need to be planned. Since the images acquired at the boundaries of the scanning areas corresponding to each scanning position often lack clarity, individual boundary images are insufficient to obtain sufficiently accurate information. Therefore, a portion of the scanning area corresponding to two adjacent scanning positions needs to overlap, i.e., bed overlap, to obtain the required boundary information. In some embodiments, a threshold for bed overlap can be preset. This threshold can take various forms, such as the absolute size of the overlapping area (e.g., 500mm * 100mm) or the relative size of the overlapping area to the scanning area (e.g., area percentage of 20%).

[0058] In some embodiments, the scanning scheme may include multiple parameters, such as the movement position of the bed and the time corresponding to the movement position.

[0059] In some embodiments, a scanning plan can be formulated and generated based on preset parameters. For example, the movement position of the bed and the corresponding time can be determined based on the injected medication, the target region of interest, etc. In some embodiments, some or all of the scanning plan can be automatically generated using algorithms or machine learning models. In some embodiments, the generated scanning plan can be modified manually or otherwise, and the modified scanning plan can be used as the final scanning plan.

[0060] The second region of interest (ROI) is a human organ / tissue of interest identified through image information, such as the heart, lungs, liver, stomach, etc. In some embodiments, the second ROI may include at least one organ / tissue of the scanned object that requires special attention based on the scanning needs, condition, etc.

[0061] Human organs / tissues can be identified and their locations obtained from image information in various ways, such as manual identification by doctors or automatic identification based on machine learning methods. In some embodiments, the second region acquisition unit 221 can automatically identify one or more human organs / tissues in an image based on medical image information such as positioning images and through machine learning models, thereby determining one or more second regions of interest, wherein the second region of interest may include the location of the target region of interest.

[0062] The second position is the relative position of the bed and the scanning device related to the target region of interest (ROI). This position ensures that the ROI of the scanned object is located in the optimal scanning position, such as the center of the scanning field of view of the scanning device. In some embodiments, the relative position of the bed and the scanning device can be changed by means of moving the bed, so that the ROI of the scanned object is located in the center of the scanning field of view of the scanning device. In some embodiments, since there may be multiple ROIs, the second position may include multiple locations, each corresponding to one ROI.

[0063] In some embodiments, the relative position of the patient and the scanning device can be determined based on the positioning image, so that the bed can be moved to a second position, that is, the relative position of the bed and the scanning device can be changed by the relative movement of the bed and the scanning device.

[0064] In some embodiments, the second position determination unit 222 can determine the scanning position corresponding to the target region of interest of the scanned object, i.e., the second position, based on the second region of interest. This position can make the target scanning imaging organ located at the center of the scanning field of view of the scanning device.

[0065] In some embodiments, the scheme determination unit 223 can determine a scanning scheme based on a second position. This scanning scheme may include the movement position of the bed, the time corresponding to the movement position, etc., thereby determining when to move to the second position and the dwell time at the second position. In some embodiments, according to the scanning scheme, the bed can be moved to a first position, and from the first position, moved to the second position, thereby enabling scanning of the target region of interest of the scanned object. The movement position of the bed may include the second position.

[0066] In some embodiments, the second positions may include multiple locations, and the scanning scheme may include determining the overlapping scanning area corresponding to two adjacent second positions of the bed based on the multiple second positions, i.e., bed overlap. In some embodiments, the overlapping area can be within a threshold value. For example, the size is not less than 500mm*100mm; or, for example, the area of ​​the overlapping area is not less than 20% of the area of ​​the scanning area corresponding to one of the second positions.

[0067] In some embodiments, the scheme determination unit 223 can determine the movement position of the hospital bed based on the second position; it can determine the time corresponding to the movement position based on the injected medication, i.e., when to move to that movement position, which organ / tissue to scan, and the duration of the scan of that organ / tissue, and then generate a scan scheme based on these determined scan parameters. In some embodiments, a partial or complete scan scheme can be automatically generated based on preset parameters and positioning images, using specific algorithms or machine learning models.

[0068] In some embodiments, the time it takes for the drug to flow through a specific organ / tissue (e.g., a first region of interest, a second region of interest, etc.) can be determined based on the drug flow rate, and the time it takes for the bed to move to the corresponding scanning position of that specific organ / tissue can be determined based on that time.

[0069] In some embodiments, the scheme determination unit 223 can determine a first time when the drug flows through the target region of interest based on the drug flow rate in the scanned object; determine a second time when the bed moves to a second position based on the first time; and generate a scanning scheme based on the second time, wherein the time corresponding to the moving position may include the second time. In some embodiments, some or all of the scanning scheme can be automatically generated based on the second time using a specific algorithm or machine learning model.

[0070] In some embodiments, the scheme determination unit 223 may determine a third time when the drug flows through the first region of interest based on the flow rate of the drug in the scanned object; and based on the third time, determine a fourth time when the bed moves to the first position, the fourth time being earlier than or equal to the second time.

[0071] Drug flow rate refers to the speed at which an injected drug flows through the human body. Because drug flow rate determines when the drug reaches which organ and how long it remains in that organ, scan time (e.g., PET scans) can be correlated with drug flow rate. Drug flow rate is related to various factors, such as the type of drug, the body's metabolic rate, and the patient's physical condition. In some embodiments, drug flow rate can be obtained based on empirical or statistical values. In other embodiments, drug flow rate can be obtained based on algorithms, machine learning models, etc.

[0072] In some embodiments, a first time can be determined based on the location of the drug injection into the scanned object, the start time of drug injection into the scanned object, the drug flow rate, and a second region of interest (e.g., a target region of interest). In some embodiments, the second time can be equal to or earlier than the first time. For example, if the injected drug flows through organ A at time t, the bed can be moved to bed position L at or before time t (e.g., when the bed is at bed position L, organ A is located at the center of the scanning field of view of the scanning device).

[0073] Step 330: Based on the scanning protocol, the hospital bed is moved to a first position, and the object to be scanned is scanned using medical imaging equipment. In some embodiments, step 330 may be performed by the acquisition module 230.

[0074] In some embodiments, a final scanning plan can be obtained based on the generated scanning plan, so that the bed is moved to a first position, i.e., the initial scanning position, and then moves to a preset scanning position, such as a second position, according to the final scanning plan. The scanning object is then scanned by the scanning device, including scanning of the target region of interest and image acquisition of the target region of interest to obtain a scanned image of the target region of interest.

[0075] In some embodiments, the generated scanning plan can be directly used as the final scanning plan. In other embodiments, the generated scanning plan can be modified and adjusted manually, for example, by adjusting the movement position of the hospital bed and the time corresponding to the movement position, and the adjusted scanning plan can be used as the final scanning plan.

[0076] To ensure that the drug flows through the designated organ at a predetermined time, the drug injection and image acquisition times need to be synchronized. In some embodiments, the medical imaging equipment can be activated using a preset method while the drug is being injected into the subject, initiating the scanning and image acquisition of the subject. The preset method may include one or a combination of foot pedal activation, gesture recognition activation, voice recognition activation, EEG recognition activation, VR / AR device activation, and device accessory activation.

[0077] In some embodiments, a foot-sensing system can be installed around the scanning device, allowing the operator to immediately initiate image acquisition by contacting the foot pedal for a certain period of time. Since drug injection requires aseptic technique, hand contact should be avoided in scenarios where only one clinical operator is involved. With the foot pedal, the clinical operator can simultaneously administer the drug and acquire images by pressing the pedal.

[0078] In some embodiments, a gesture sensing system can be installed around the scanning device. This system can automatically recognize the action of drug injection and immediately initiate image acquisition upon recognition of the drug injection action. In some embodiments, the gesture that triggers image acquisition can be a preset gesture in the gesture sensing system, which can immediately initiate image acquisition upon recognition of the preset gesture. In some embodiments, machine learning methods can be used to recognize the action of drug injection.

[0079] In some embodiments, voice commands from the drug injection operator can be directly recognized via voice recognition, allowing image acquisition to begin simultaneously with drug injection. For example, a voice recognition system can be installed in the scanning room. This system can automatically recognize the voice commands of the clinical operator. In situations where only one clinical operator is present, the operator can use voice commands to instruct the scanning device to immediately begin scanning while the drug is being injected. As another example, the drug injection operator can issue acquisition commands to the acquisition device operator simultaneously with drug injection via voice (e.g., through a wireless microphone).

[0080] In some embodiments, a voice recognition system can also guide the drug injection operator during the injection process. Specifically, the voice recognition system can inform the operator in the scanning room when the system will begin data collection and instruct them to administer the drug injection according to the system's start time. In some embodiments, the operator can confirm the start time of data collection by adding a countdown reminder or a notification sound at the moment the system begins data collection, thus ensuring consistency between the injection and data collection times.

[0081] In some embodiments, an EEG acquisition and sensing system can be installed on the injection operator to acquire and identify the operator's brainwaves, and to simultaneously initiate image acquisition based on the identified drug injection action or the acquisition command issued by the operator's brain.

[0082] In some embodiments, data acquisition can be initiated by operating the images displayed on a VR / AR device. For example, a drug injection operator can use the images displayed on the VR / AR device to identify the injection and data acquisition environment, and operate the injection of medication and image acquisition. As another example, a data acquisition device operator can use the synchronously displayed VR / AR image of the drug injection operator to identify the injection actions of the drug injection operator and simultaneously initiate data acquisition.

[0083] In some embodiments, drug injection and image acquisition can be started simultaneously via device accessories (e.g., buttons, keys, switches, etc.). For example, an acquisition start button can be set within the operating range of the injection operator.

[0084] In some embodiments, drug injection and image acquisition can also be started simultaneously in other ways. For example, the drug injection operator can start image acquisition by directly touching a touchscreen covered by a disposable antibacterial cover.

[0085] In some embodiments of this specification, patients can be automatically moved to designated positions by automatically identifying organs in medical images. Based on pre-set parameters, organs in medical images can be automatically identified and a scanning plan can be planned, allowing the patient to be positioned precisely for scanning. This improves the accuracy of patient positioning, reduces the burden on clinical operators, and saves human resources. It avoids the problem of inaccurate parameters in the planned scanning plan caused by subjective factors such as insufficient clinical operator experience, thereby reducing the probability of patients needing rescanning and significantly reducing the radiation dose received by patients, protecting their health. Furthermore, through continuous iteration of machine learning models, execution efficiency and plan accuracy can be continuously improved. By initiating image acquisition simultaneously with drug injection in various ways, it ensures that clinical operators can complete the task of synchronously starting image acquisition without affecting drug injection, solving the problem that drug injection and image acquisition could not be guaranteed to occur simultaneously in previous workflows such as contrast-enhanced scanning. This ensures the accurate and high-quality execution of the scanning work.

[0086] Figure 4 This is a schematic diagram of an automatic planning scan method according to some embodiments of this specification.

[0087] In some embodiments, the first position of the hospital bed can be determined based on the positioning image of the scanned object.

[0088] like Figure 4As shown, in some embodiments, the location of the first region of interest in the patient positioning image 401 can be obtained by automatically identifying it using a machine learning model in step 410. In some embodiments, the first region of interest may be an organ / tissue associated with a medical scan using injected drugs, such as contrast-enhanced scanning, for example, the aortic blood pool region 411.

[0089] Figure 5A , Figure 5B This is a schematic diagram illustrating the automatic identification of the aortic blood pool region and the automatic bed transfer to the corresponding position, according to some embodiments of this specification. For example... Figure 5A As shown, regions 510, 520 and 530 are the aortic blood pool regions 411 identified from the localization images.

[0090] In some embodiments, the relative position of the patient and the scanning device can be determined based on a pre-determined first region of interest (ROI), and the bed can be automatically moved to a first position via operations such as bed relocation. At this position, the ROI (e.g., aortic blood pool region 411) can be located at the center of the scanning field of view of the scanning device; and image acquisition of the target ROI begins from this position. Figure 5B The image shown is a user image obtained after moving to the first position. The area between the two light-colored parallel lines (area 540) includes the first region of interest, namely the aortic blood pool region 411.

[0091] In some embodiments, a scanning plan can be generated based on preset parameters (e.g., the drug injected into the scanned object, the target region of interest of the scanned object, etc.), wherein the scanning plan may include at least one of the moving position of the bed and the time corresponding to the moving position.

[0092] like Figure 4 As shown, in some embodiments, a drug and organ list can be set before the scan begins via step 420. The drug refers to medication that needs to be injected during the scan, typically used for PET scans, etc. The organs in the organ list are target regions of interest determined according to the purpose of the scan.

[0093] like Figure 4 As shown, in some embodiments, the organ 412 in the patient positioning image 401, i.e. the second region of interest, can be automatically identified by a machine learning model through step 410. The second region of interest may include the target region of interest.

[0094] In some embodiments, organs / tissues in the patient localization image 401 can be automatically identified by a machine learning model, and then a target region of interest (e.g., organ 412), i.e. a second region of interest, can be determined from the identified organs / tissues based on the organ list.

[0095] In some embodiments, the localization image 401 and the organ list can be directly input into the machine learning model to obtain the output target region of interest (e.g., organ 412), i.e., the second region of interest.

[0096] Figure 6A , Figure 6B This is a schematic diagram illustrating the automatic identification of a patient's major organs and the automatic planning of a scanning protocol, according to some embodiments of this specification. For example... Figure 6A As shown, region 610 is the second region of interest identified based on the positioning image 401.

[0097] In some embodiments, a scanning plan can be automatically generated based on pre-set parameters. For example... Figure 4 As shown, in some embodiments, step 430 can be executed based on the aortic blood pool region 411, organ 412 and the list of drugs and organs set in step 420. The scanning scheme can be automatically planned by the model and may include scanning time 431, scanning range 432 and bed overlap 433, etc. The model may be a machine learning model or a model obtained according to a specific algorithm.

[0098] The scanning range refers to the area covered by the scan, such as the patient's whole body or a part of their body. The scanning range can correspond to the scanning position. Scanning time refers to the time it takes for the bed to reach a scanning position and / or the time it stays at that position, such as the scan start time, scan duration, and scan end time. Scanning time is related to the relative speed of movement between the bed and the scanning equipment. Bed overlap refers to overlapping the scanning ranges corresponding to two adjacent scanning positions of the scanned object. For example, overlapping the scanning ranges of the lungs and liver, ensuring the overlapping area is within a threshold range.

[0099] In some embodiments, the movement position of the bed (e.g., a first position) can be determined based on the location of the aortic blood pool region 411. When the bed is moved to the first position, the aortic blood pool region 411 may be located at the center of the scanning field of view of the scanning device.

[0100] In some embodiments, the movement position of the bed (e.g., a second position) can be determined based on the position of the organ 412. When the bed is moved to the second position, the organ 412 may be located at the center of the scanning field of view of the scanning device.

[0101] In some embodiments, the drug flow rate can be obtained based on various factors such as the drug set in step 420, and the time corresponding to the bed moving to the second position can be determined based on the drug flow rate (e.g., at what time the bed moves to the second position).

[0102] like Figure 6BAs shown, the organ list set in step 440 includes the lungs, liver, and pelvis. Boxes 620, 630, and 640 represent the planned scanning ranges (scanning areas) corresponding to the second positions of the lungs, abdomen, and pelvis, respectively. When the scanning range is the position corresponding to boxes 620, 630, and 640, the corresponding organ is located at the center of the scanning field of view of the scanning device. There is an overlap between 620 and 630, and there is also an overlap between 530 and 640, i.e., bed overlap.

[0103] In some embodiments, a final scanning scheme can be determined based on the generated scheme, and image acquisition can begin according to the final scanning scheme. In some embodiments, the generated scheme can be used as the final scanning scheme. Figure 4 As shown, in some embodiments, step 460 can be executed using a preset method based on planned scanning parameters such as scanning time 451, scanning range 452, and bed overlap 453, while drug injection and image acquisition are started simultaneously.

[0104] In some embodiments, the generated scanning scheme can be modified manually or otherwise to determine the final scanning scheme.

[0105] In some embodiments, before acquiring images of the target region of interest (e.g., organ 412), the bed can be moved to a first position corresponding to the first region of interest (e.g., aortic blood pool region 411) according to the scan time 451, and then the bed can be moved to a second position from the first position to begin acquiring images of the target region of interest.

[0106] It should be noted that the above descriptions of processes 300 and 400 are for illustrative purposes only and do not limit the scope of this specification. Those skilled in the art can make various modifications and changes to processes 300 and 400 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, the identification of the first and second regions of interest based on the localization image can be performed by different models, and can be performed sequentially or simultaneously. As another example, the identification of the localization image and the planning of the scanning scheme can be performed by the same model or by different models.

[0107] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0108] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0109] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0110] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0111] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0112] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0113] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for automatically planning a scan, comprising: Determine the first position of the hospital bed based on the localization image of the scanned object; A second location is determined based on preset parameters and the positioning image. The preset parameters include at least one of the drug injected into the scanned object and a target region of interest of the scanned object. There are multiple second locations, each corresponding to a target region of interest. Determining the second location based on the preset parameters and the positioning image includes: Based on the preset parameters and the positioning image, at least one second region of interest of the scanned object is obtained; The second position is determined based on the at least one second region of interest; Based on the second position, a scanning plan is generated, the scanning plan including the movement position of the bed and the time corresponding to the movement position, and the scanning plan is related to the flow rate of the drug in the scanned object; Based on the scanning scheme, the hospital bed is controlled to move to the first position; Based on the scanning scheme, the hospital bed is controlled to move to multiple second positions, and the medical imaging equipment is controlled to start scanning the target region of interest. The time it takes for the hospital bed to move to each second position is determined based on the flow rate of the drug in the scanned object. When the hospital bed is in any second position, the target region of interest corresponding to that second position is located at the center of the scanning field of view of the medical imaging equipment.

2. The method of claim 1, wherein determining the first position of the hospital bed based on the positioning image of the scanned object comprises: Based on the localization image, a first region of interest of the scanned object is obtained through a machine learning model; The first location is determined based on the first region of interest.

3. The method as described in claim 1, wherein obtaining at least one second region of interest of the scanned object based on the preset parameters and the positioning image comprises: Based on the preset parameters and the positioning image, at least one second region of interest of the scanned object is obtained through a machine learning model, wherein the second region of interest includes the target region of interest.

4. The method of claim 1, wherein generating the scanning scheme based on the second position comprises: The first time the drug flows through the target region of interest is determined based on the flow rate of the drug in the scanned object. Based on the first time, determine the second time at which the hospital bed moves to the second position; The scanning scheme is generated based on the second time.

5. The method of claim 1, wherein the scanning scheme includes determining a scanning overlap region corresponding to two adjacent second positions of the bed based on a plurality of second positions.

6. The method of claim 1, wherein controlling the medical imaging device to begin scanning the target region of interest comprises: While injecting the drug into the object being scanned, the medical imaging device is activated to scan the object using a preset method, which includes at least one of the following: foot pedal activation, gesture recognition activation, voice recognition activation, EEG recognition activation, VR / AR device activation, and device accessory activation.

7. An automatic scanning planning system, comprising a positioning module, a planning module, and a data acquisition module; The positioning module is used to determine the first position of the hospital bed based on the positioning image of the scanned object; The scheme module is used for A second location is determined based on preset parameters and the positioning image. The preset parameters include at least one of the drug injected into the scanned object and a target region of interest of the scanned object. There are multiple second locations, each corresponding to a target region of interest. Determining the second location based on the preset parameters and the positioning image includes: Based on the preset parameters and the positioning image, at least one second region of interest of the scanned object is obtained; The second position is determined based on the at least one second region of interest; Based on the second position, a scanning plan is generated, the scanning plan including the movement position of the bed and the time corresponding to the movement position, and the scanning plan is related to the flow rate of the drug in the scanned object; The acquisition module is used for Based on the scanning scheme, the hospital bed is controlled to move to the first position; Based on the scanning scheme, the hospital bed is controlled to move to multiple second positions, and the medical imaging equipment is controlled to start scanning the target region of interest. The time it takes for the hospital bed to move to each second position is determined based on the flow rate of the drug in the scanned object. When the hospital bed is in any second position, the target region of interest corresponding to that second position is located at the center of the scanning field of view of the medical imaging equipment.

8. An apparatus for automatically planning a scan, comprising a processor for performing the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer performs the method as described in any one of claims 1 to 6.

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