A system and method for determining a set-up position of a nuclear magnetic resonance instrument

Abstract

This specification provides a system and method for determining the placement position of an MRI scanner. The method includes: acquiring a three-dimensional spatial model of a scanning chamber for placing the MRI scanner, the scanning chamber including one or more other devices; determining at least two candidate placement areas for the MRI scanner based on the three-dimensional spatial model; determining the physical field distribution of the scanning chamber when the MRI scanner operates in each of the at least two candidate placement areas; and determining the placement position of the MRI scanner based on the physical field distribution corresponding to the at least two candidate placement areas.

Description

Technical Field

[0001] This specification relates to the field of medical technology, and in particular to a system and method for determining the placement of an MRI scanner. Background Technology

[0002] Magnetic Resonance Imaging (MRI) scanners generate high-intensity physical fields (e.g., magnetic fields, electromagnetic fields) during operation, which can affect surrounding objects. The impact of these fields varies depending on the object being scanned; therefore, the MRI scanner needs to be positioned appropriately within the scanning room to minimize or avoid the influence of the field intensity on other objects. With the development of MRI technology, the physical field intensity of MRI scanners is increasing, leading to greater impact on other objects in the scanning room and making the precise placement of the MRI scanner increasingly difficult. Therefore, this specification aims to provide an efficient and accurate system and method for determining the placement of an MRI scanner within the scanning room. Summary of the Invention

[0003] One embodiment of this specification provides a method for determining the placement location of an MRI scanner. The method includes: acquiring a three-dimensional spatial model of a scanning chamber for placing the MRI scanner, the scanning chamber including one or more other devices; determining at least two candidate placement areas for the MRI scanner based on the three-dimensional spatial model; for each of the at least two candidate placement areas, determining the physical field distribution of the scanning chamber when the MRI scanner operates in the candidate placement area; and determining the placement location of the MRI scanner based on the physical field distribution corresponding to the at least two candidate placement areas.

[0004] In some embodiments, determining the placement position of the MRI scanner based on the physical field distribution corresponding to the at least two candidate placement areas includes one or more iterations, wherein each iteration in at least one iteration includes: determining at least one target placement area from the at least two candidate placement areas based on the physical field distribution corresponding to the at least two candidate placement areas; determining at least two sub-regions of each of the at least one target placement area; determining whether a termination condition is met; and in response to the termination condition being met, selecting one of the at least two sub-regions as the placement position of the MRI scanner; or in response to the termination condition not being met, designating the at least two sub-regions as at least two candidate placement areas in the next iteration.

[0005] In some embodiments, determining at least one target placement area from the at least two candidate placement areas based on the physical field distributions corresponding to the at least two candidate placement areas includes: for each of the at least two candidate placement areas, determining the total physical field intensity of the other equipment when the MRI scanner is running in the candidate placement area based on its corresponding physical field distribution; and determining the at least one target placement area based on the total physical field intensity corresponding to the at least two candidate placement areas.

[0006] In some embodiments, determining at least one target placement area from the at least two candidate placement areas based on the physical field distributions corresponding to the at least two candidate placement areas includes: determining the at least one target placement area using a placement area determination model based on the physical field distributions corresponding to the at least two candidate placement areas.

[0007] In some embodiments, determining at least two candidate placement regions for the nuclear magnetic resonance spectrometer based on the three-dimensional spatial model includes: determining at least two blocks from at least a portion of the three-dimensional spatial model; and determining the at least two candidate placement regions from the at least two blocks.

[0008] In some embodiments, at least two of the at least two blocks have overlapping regions.

[0009] In some embodiments, determining at least two blocks from at least a portion of the three-dimensional spatial model includes: acquiring reference data of the scanning chamber, the reference data relating at least to the one or more other placed devices; determining an exclusionary region in the three-dimensional spatial model based on the reference data; and determining at least two blocks from at least a portion of the three-dimensional spatial model based on the exclusionary region.

[0010] In some embodiments, determining at least two blocks from at least a portion of the three-dimensional spatial model based on the excluded region includes: obtaining a reference position of a reference MRI scanner in a reference scanning chamber; and determining at least two blocks from a portion of the three-dimensional spatial model excluding the excluded region based on the reference position of the reference MRI scanner in the reference scanning chamber.

[0011] In some embodiments, the method of claim 1, wherein determining the physical field distribution of the scanning chamber when the MRI scanner is running in the candidate placement area comprises: acquiring feature information of the MRI scanner running in the candidate placement area, the feature information including at least the physical field emission model of the MRI scanner; and determining the physical field distribution in the scanning chamber when the MRI scanner is running in the candidate placement area based on the three-dimensional spatial model and the feature information, using a physical field simulation model.

[0012] In some embodiments, the physical field includes at least one of a magnetic field and an electromagnetic field.

[0013] One embodiment of this specification provides a system for determining the placement position of an MRI scanner. The system includes an acquisition module, a placement area determination module, a physical field distribution determination module, and a placement position determination module. The acquisition module acquires a three-dimensional spatial model of a scanning chamber for placing the MRI scanner, the scanning chamber including one or more other devices. The placement area determination module determines at least two candidate placement areas for the MRI scanner based on the three-dimensional spatial model. The physical field distribution determination module determines the physical field distribution of the scanning chamber in each of the at least two candidate placement areas, when the MRI scanner operates in that candidate placement area. The placement position determination module determines the placement position of the MRI scanner based on the physical field distributions corresponding to the at least two candidate placement areas.

[0014] Some additional features of this specification may be described in the following description. These additional features will become apparent to those skilled in the art from the following description and the accompanying drawings, or from an understanding of the production or operation of the embodiments. The features of this specification can be implemented and obtained by practice or use of various aspects of the methods, means, and combinations set forth in the detailed examples below. Attached Figure Description

[0015] 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:

[0016] Figure 1 This is a schematic diagram illustrating the application scenario of the system based on exemplary placement positions shown in some embodiments of this specification;

[0017] Figure 2 This is a schematic diagram of the system based on exemplary placement positions shown in some embodiments of this specification;

[0018] Figure 3This is a schematic flowchart illustrating an exemplary method for determining the placement of an MRI scanner according to some embodiments of this specification;

[0019] Figure 4 This is a schematic flowchart illustrating an exemplary method for determining the placement of an MRI scanner, based on some embodiments of this specification. Detailed Implementation

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] Figure 1 This is a schematic diagram illustrating the application scenario 100 of the system based on exemplary placement positions shown in some embodiments of this specification. For example... Figure 1As shown, application scenario 100 may include a scanning chamber 110, an MRI scanner 120, and a processing device 130. A placement determination system can be used to determine the placement position of the MRI scanner 120 within the scanning chamber 110. In some embodiments, the processing device 130 may be disposed within the scanning chamber 110. In some embodiments, the processing device 130 may be a part of the MRI scanner 120. The connections between the components in application scenario 100 may be variable. For example... Figure 1 As shown, the MRI scanner 120 can be connected to the processing device 130 via a network. Alternatively, the MRI scanner 120 can be directly connected to the processing device 130.

[0025] The MRI scanner 120 can be any medical device that utilizes the magnetic resonance phenomenon. In some embodiments, the MRI scanner 120 can scan a target object within a detection area or scanning area to obtain scan data of the target object. In some embodiments, the MRI scanner 120 may include a magnetic resonance imaging (MRI) scanner, an X-ray imaging-MRI scanner, a single-photon emission computed tomography-MRI scanner, a digital subtraction angiography-MRI scanner, etc. In some embodiments, the processing device 130 may be integrated into the MRI scanner 120, or the MRI scanner 120 and the processing device 130 may function through the same entity. The MRI scanner 120 described above is for illustrative purposes only and is not intended to limit the scope of this specification.

[0026] When an MRI scanner is in operation, it emits magnetic fields (e.g., the main magnetic field generated by the main magnet) and electromagnetic fields (e.g., the gradient field generated by the gradient system) into its scanning chamber. The physical field in this specification includes at least one of magnetic and electromagnetic fields. For example, a physical field may include a magnetic field. As another example, a physical field may include the sum of a magnetic field and an electromagnetic field (e.g., the maximum electromagnetic field).

[0027] Processing device 130 can process data and / or information related to the MRI scanner 120 or other components (e.g., storage devices). For example, such as Figure 1As shown, the MRI scanner 120 is the device to be placed in the scanning chamber 110. The scanning chamber 110 includes one or more other devices, such as storage cabinets, air conditioners, control devices, and cabinet units. The processing device 130 can determine the placement position of the MRI scanner 120 in the scanning chamber 110 based on information related to the MRI scanner 120, the scanning chamber 110, and / or other devices in the scanning chamber 110. As an example only, the processing device 130 can acquire a three-dimensional spatial model of the scanning chamber 110. Based on the three-dimensional spatial model of the scanning chamber 110, the processing device 130 can determine at least two candidate placement areas in the scanning chamber 110 for placing the MRI scanner 120. For each candidate placement area, the processing device 130 can determine the physical field distribution of the scanning chamber 110 when the MRI scanner 120 is running in that candidate placement area. The processing device 130 can further determine the placement position of the MRI scanner 120 in the scanning chamber 110 based on the physical field distributions corresponding to the at least two candidate placement areas. In some embodiments, the processing device 130 may be local or remote. For example, the processing device 130 may access information and / or data from the MRI scanner 120 via a network.

[0028] It should be noted that application scenario 100 is provided for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can make various modifications or variations based on the description in this specification. For example, application scenario 100 may also include storage devices, networks, virtual reality devices, etc. Storage devices can be used to store information and / or data acquired or generated by one or more components in application scenario 100. Networks can include any suitable network capable of facilitating information and / or data exchange. Virtual reality devices can be devices capable of implementing Virtual Reality (VR) technology, such as VR glasses, VR headsets, VR goggles, etc. For example, a user can view a three-dimensional virtual space in a scanning room through a virtual reality device. As another example, a user can virtually manipulate the three-dimensional virtual space presented by the virtual reality device through control components.

[0029] In some embodiments, at least one component of application scenario 100 (e.g., MRI scanner 120, processing device 130) can exchange information and / or data with at least one other component in application scenario 100 via a network. For example, application scenario 100 may implement similar or different functions on other devices. However, these changes and modifications will not depart from the scope of this specification.

[0030] Figure 2 This is a schematic diagram of the system based on exemplary placement positions shown in some embodiments of this specification.

[0031] like Figure 2As shown, in some embodiments, system 200 may include an acquisition module 210, a placement area determination module 220, a physical field distribution determination module 230, and a placement position determination module 240. In some embodiments, the functions corresponding to system 200 may be executed by processing device 130; for example, acquisition module 210, placement area determination module 220, physical field distribution determination module 230, and placement position determination module 240 may be modules within processing device 130.

[0032] The acquisition module 210 can be used to acquire information related to the placement position determination system 100. For example, the acquisition module 210 can acquire a three-dimensional spatial model of the scanning chamber. The scanning chamber may include one or more other placed devices. For a description of acquiring the three-dimensional spatial model of the scanning chamber, please refer to [link to relevant documentation]. Figure 3 Step 310 in the above steps will not be repeated here.

[0033] The placement area determination module 220 can determine at least two candidate placement areas for the MRI scanner based on a three-dimensional spatial model. The candidate placement areas refer to areas within the scanning chamber that can be used to place the MRI scanner. In some embodiments, the placement area determination module 220 can determine at least two blocks from at least a portion of the three-dimensional spatial model. Each block can correspond to a physical area within the scanning chamber. Further, the placement area determination module 220 can determine at least two candidate placement areas from the at least two blocks. For a more detailed description of determining at least two candidate placement areas for the MRI scanner based on a three-dimensional spatial model, please refer to [link to relevant documentation]. Figure 3 Step 320 in the process will not be repeated here.

[0034] The physical field distribution determination module 230 can be used to determine the physical field distribution of the scanning chamber of the MRI scanner when it is running in each of at least two candidate placement areas. The physical field distribution can display the distribution of the physical field in the scanning chamber when the MRI scanner is running, i.e., the distribution pattern of the physical field intensity at different locations within the scanning chamber during MRI scanner operation. For a more detailed description of determining the physical field distribution of the scanning chamber of the MRI scanner when it is running in each of at least two candidate placement areas, please refer to [link to relevant documentation]. Figure 3 Step 330 in the previous section will not be repeated here.

[0035] The placement location determination module 240 can be used to determine the placement location of the MRI scanner based on the physical field distribution corresponding to at least two candidate placement areas. In some embodiments, the placement location determination module 240 can determine at least one target placement area from the candidate placement areas based on the physical field distribution corresponding to at least two candidate placement areas. Further, the placement location determination module 240 can determine the placement location of the MRI scanner based on at least one target placement area. For more details on determining the placement location of the MRI scanner based on the physical field distribution corresponding to at least two candidate placement areas, please refer to [link to relevant documentation]. Figure 3 Step 340 in the process will not be repeated here.

[0036] It should be understood that Figure 2 The placement determination system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented through hardware, software, or a combination of both.

[0037] It should be noted that the above description of the system and its modules is for illustrative purposes only and should not be construed as limiting this specification to the scope of the illustrated embodiments. It is understood that those skilled in the art, after understanding the principles of this system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from these principles. For example, in some embodiments, Figure 2 The modules disclosed above can be different modules within a single system, or a single module can implement the functions of two or more of the modules described above. For example, the modules can share a single storage module, or each module can have its own separate storage module. Such variations are all within the scope of protection of this specification. Furthermore, in some embodiments, one or more modules in system 200 can be implemented by other systems. That is, the aforementioned one or more modules may not be included in system 200.

[0038] Figure 3 This is a schematic flowchart illustrating an exemplary method for determining the placement of an MRI scanner according to some embodiments of this specification. In some embodiments, one or more steps of process 300 may be performed... Figure 1 The application scenario 100 shown is implemented or is provided by Figure 2 The placement shown determines the execution of system 200. For example, process 300 can be executed by a module within processing device 130. Figure 3 As shown, process 300 may include the following steps.

[0039] Step 310: Obtain a three-dimensional spatial model of the scanning chamber for placing the MRI scanner, the scanning chamber including one or more other devices. In some embodiments, step 310 may be performed by the processing device 130 or the acquisition module 210.

[0040] A scanning room can be a room used to house an MRI scanner, a medical equipment system. An MRI scanner can be any medical device that utilizes the magnetic resonance phenomenon, for example... Figure 1 The MRI scanner 120 is shown. The three-dimensional spatial model of the scanning chamber refers to a three-dimensional model representing the internal scene of the scanning chamber. In some embodiments, the three-dimensional spatial model of the scanning chamber can be used to represent the internal spatial structure of the scanning chamber and one or more other devices placed within the scanning chamber. The one or more other devices may include other devices already placed in the scanning chamber and / or other devices to be placed in the scanning chamber. Exemplary examples of one or more other devices may include cabinets, computing devices, control devices, storage cabinets, air conditioners, etc.

[0041] In some embodiments, the processing device 130 can acquire point cloud data of the scanning chamber. Each data point in the point cloud data can correspond to a physical point or a region within the scanning chamber's interior scene. The data points in the point cloud data can include information about their corresponding physical point (or physical region), such as the location of the physical point, the object to which the physical point belongs, etc. In some embodiments, the point cloud data can be acquired by a sensor (e.g., LiDAR). For example, the sensor can emit laser pulses to scan the interior space of the chamber. The laser pulses can be reflected by physical points (or physical regions) in the interior space of the scanning chamber and returned to the sensor. The sensor can generate point cloud data representing the scanning chamber based on one or more features of the returned laser pulses. In some embodiments, during the point cloud data collection process, the sensor can rotate within a scanning angle range (e.g., 360 degrees, 180 degrees, 120 degrees) and scan the interior space of the scanning chamber at a specific scanning frequency (e.g., 10Hz, 15Hz, 20Hz). The processing device 130 can construct at least a portion of a three-dimensional spatial model of the scanning chamber based on the point cloud data. For example, processing device 130 can construct a 3D model of the interior space of the scanning chamber and one or more objects located inside the scanning chamber based on the coordinate system and scale of the 3D spatial model and information contained in the point cloud data. In some embodiments, if other devices are not yet placed in the scanning chamber, processing device 130 can generate a 3D model of the device based on data related to that device (such as point cloud data). Processing device 130 can also fuse the 3D model of the device with the 3D spatial model of the scanning chamber based on the location where the device will be placed in the scanning chamber.

[0042] In some embodiments, the three-dimensional spatial model of the scanning chamber can be generated based on multiple two-dimensional images. In some embodiments, the multiple two-dimensional images can be images captured in advance. The processing device 130 can obtain a three-dimensional spatial model based on the multiple pre-captured two-dimensional images using three-dimensional reconstruction techniques. Exemplary three-dimensional reconstruction techniques may include texture shape (SFT) methods, shading reconstruction three-dimensional shape methods, multi-view stereo (MVS) methods, structure motion reconstruction (SFM) methods, time-of-flight (ToF) methods, structured light methods, moiré schlieren methods, etc., or any combination thereof.

[0043] In some embodiments, the processing device may also acquire the three-dimensional spatial model in other ways. For example, a depth camera can be used to collect depth data of a scanned indoor scene. The processing device 130 can acquire the three-dimensional spatial model based on the depth data of the scanning room. Alternatively, the three-dimensional spatial model can be acquired using methods such as manual drawing or surveying; this specification does not impose any limitations on these methods.

[0044] In some embodiments, the three-dimensional spatial model may also be generated in advance and stored in a storage device or a database. The processing device 130 can retrieve the three-dimensional spatial model of the scanning chamber from the storage device or the database.

[0045] Step 320: Based on the three-dimensional spatial model, at least two candidate placement areas for the nuclear magnetic resonance spectrometer are determined. In some embodiments, step 320 may be performed by the processing device 130 or the placement area determination module 220.

[0046] The candidate placement area for the MRI scanner refers to an area within the scanning chamber that can be used to place the MRI scanner. In some embodiments, the processing device 130 can determine at least two blocks from at least a portion of a three-dimensional spatial model. Each block may correspond to a physical area within the scanning chamber. Further, the processing device 130 can determine at least two candidate placement areas from the at least two blocks.

[0047] In some embodiments, the processing device 130 may acquire reference data of the scanning chamber and determine exclusion zones from a three-dimensional spatial model based on the reference data. Exclusion zones correspond to areas within the scanning chamber where it is impossible or difficult to place an MRI scanner. As an example only, the reference data for the scanning chamber includes at least data related to one or more other devices (e.g., location and size data). The processing device 130 may determine areas where other devices have been placed or will be placed as exclusion zones from the three-dimensional spatial model based on data from these other devices.

[0048] In some embodiments, the reference data for the scanning room may further include data related to the user's activity range within the scanning room. For example, the processing device 130 may determine a reference area in the three-dimensional spatial model corresponding to the user's activity range within the scanning room based on the correspondence between the three-dimensional spatial model and the internal space of the scanning room, and exclude this area. Optionally, the user may manually determine the reference area in the three-dimensional spatial model corresponding to the user's activity range within the scanning room. For example, the user may directly draw the reference area in the three-dimensional spatial model using a display device. Alternatively, the processing device 130 may determine the reference area in the three-dimensional spatial model corresponding to the user's activity range based on the user's historical activity trajectory in the scanning room or other reference scanning rooms. In some embodiments, the reference area corresponding to the user's activity range may be a two-dimensional reference area on the ground.

[0049] After determining the exclusionary region, the processing device 130 can accordingly determine at least two blocks from at least a portion of the three-dimensional spatial model. In some embodiments, the processing device 130 can determine at least two blocks from the remaining region of the three-dimensional spatial model excluding the exclusionary region. Alternatively, considering that the MRI scanner will be placed on the ground, to reduce computational load, the processing device 130 can determine at least two blocks only from the portion of the three-dimensional spatial model corresponding to the floor of the scanning chamber. As an example only, the exclusionary region can be mapped to the ground to determine the exclusionary region on the ground, and then the portion of the ground excluding the exclusionary region can be divided into blocks. For high-altitude exclusionary regions that are off the ground, the processing device 130 can first determine whether the height of the region above the ground exceeds a threshold (e.g., the height of the MRI scanner). If it does not exceed the threshold, the exclusionary region can be projected onto the ground to determine its exclusionary region on the ground. If it exceeds the threshold, the exclusionary region can be left unprocessed.

[0050] In some embodiments, the size of each block corresponding to a physical area in the scanning chamber can be greater than or equal to the size of the MRI scanner. Based on reference data of the scanning chamber, areas in the 3D spatial model unsuitable for placing the MRI scanner can be excluded, and the remaining suitable areas are then further divided into blocks. This method ensures that all obtained blocks are suitable for placing the MRI scanner, while reducing the number of blocks and consequently reducing subsequent data processing (e.g., the amount of data input to the model can be significantly reduced), improving the efficiency of determining the MRI scanner's placement location, and saving computational resources.

[0051] In some embodiments, the identified at least two blocks may not overlap. Alternatively, two or more blocks may have overlapping regions. For example, two adjacent blocks in at least two blocks may have partially overlapping regions. As another example, multiple consecutive blocks in at least two blocks may have partially overlapping regions.

[0052] In some embodiments, the processing device 130 can obtain the reference position of a reference MRI scanner in a reference scanning chamber. Based on the reference position of the reference MRI scanner in the reference scanning chamber, the processing device 130 can determine at least two blocks from a portion of the three-dimensional spatial model, excluding exclusion zones. The reference MRI scanner can have similar dimensions and physical field characteristics to the MRI scanner to be placed. For example, the reference MRI scanner can be of the same model as the MRI scanner to be placed. As an example only, the reference position of the reference MRI scanner in the reference scanning chamber is the central region of the reference scanning chamber. The processing device 130 can expand the portion of the three-dimensional spatial model corresponding to the central region of the scanning chamber outwards by a certain area and designate this expanded area as the target region. Compared to other regions outside the target region, the processing device 130 can divide the target region into more dense blocks. That is, the overlap between adjacent blocks within the target region can be larger than the overlap between adjacent blocks outside the target region. By dividing key areas in the 3D spatial model into dense blocks based on the reference position of the reference NMR spectrometer in the reference scanning chamber, the accuracy of the obtained candidate placement areas can be improved.

[0053] After at least two blocks are determined, the processing device 130 can determine at least two candidate placement areas from the at least two blocks. In some embodiments, the processing device 130 can directly designate the physical areas in the scanning chamber corresponding to the at least two blocks as the at least two candidate placement areas. In some embodiments, the processing device 130 can designate the physical areas in the scanning chamber corresponding to blocks whose distance from the target area is less than a certain threshold as candidate placement areas. For example, the processing device 130 can designate blocks located within the target area as at least two candidate placement areas. In some embodiments, the user can manually determine at least two candidate placement areas.

[0054] In some embodiments, the processing device 130 may divide a three-dimensional spatial model or a portion thereof (e.g., a portion corresponding to the ground) into at least two blocks. The processing device 130 may determine exclusionary regions based on reference information. Further, the processing device 130 may determine at least two candidate placement regions from the at least two blocks, wherein the candidate placement regions are blocks that do not contain exclusionary regions.

[0055] Step 330: For each of at least two candidate placement areas, determine the physical field distribution of the scanning chamber when the MRI scanner is operating in that candidate placement area. In some embodiments, step 330 may be performed by processing device 130 or physical field distribution determination module 230.

[0056] The physical field distribution can show the distribution of physical fields in the scanning chamber when the MRI scanner is running, that is, the distribution pattern of physical field intensity at different locations in the scanning chamber when the MRI scanner is running. When the MRI scanner is running, it emits physical fields in all directions. These physical fields may be reflected or absorbed by other objects in the scanning chamber or by the internal spatial structure of the scanning chamber (e.g., walls), forming the final physical field distribution.

[0057] In some embodiments, for each of at least two candidate placement areas, the processing device 130 may assume that the MRI scanner is placed in that candidate placement area (e.g., at the center of the candidate placement area) and predict the physical field distribution in the scanning room when the MRI scanner is operating at that location based on analysis of data related to the MRI scanner and the scanning room. For example, the physical field distribution in the scanning room when the MRI scanner is operating can be determined by the processing device 130 based on existing physical field simulation algorithms. In some embodiments, the physical field distribution in the scanning room when the MRI scanner is operating can be determined based on the Finite Element Analysis (FEA) algorithm. Finite Element Analysis-based physical field simulation mainly decomposes a 2D or 3D environment representation into a series of nodes or points. In each calculation, the values ​​of adjacent nodes or points need to be calculated, and the physical field distribution is determined through iterative calculations using a series of different algorithms.

[0058] For example, the processing device 130 can acquire characteristic information of the MRI scanner operating in the candidate placement area. This characteristic information includes at least a physical field emission model of the MRI scanner. The physical field emission model can represent the physical field emission characteristics of the MRI scanner during operation. For example, the physical field emission model can include the intensity of the physical fields emitted by the MRI scanner at different distances during operation. In some embodiments, the physical field emission model can include the intensity of the magnetic field emitted by the MRI scanner at different distances during operation (e.g., the intensity of the magnetic field emitted by the main magnet). In some embodiments, the physical field emission model can include the sum of the intensity of the magnetic field emitted by the MRI scanner at different distances during operation and the intensity of the maximum electromagnetic field that can be emitted (e.g., the maximum gradient field intensity). Based on the three-dimensional spatial model and the characteristic information, the processing device 130 can use a physical field simulation model to determine the physical field distribution in the scanning chamber when the MRI scanner is operating in the candidate placement area. For example, the processing device 130 can acquire the electromagnetic characteristics of each region in the scanning chamber based on the three-dimensional spatial model. The electromagnetic characteristics of a region can include at least one of the physical field absorption characteristics and physical field reflection characteristics of that region, such as physical field absorptivity and physical field reflectivity. The physical field simulation model can be a model used to determine the distribution of physical fields. The physical field simulation model can include convolutional neural networks (CNNs), residual networks (ResNets), etc. In some embodiments, the processing device 130 can input the physical field emission model of the MRI scanner and the electromagnetic characteristics of various regions in the scanning chamber into the physical field simulation model. The physical field simulation model can output information related to the distribution of physical fields. For example, the physical field simulation model can output the physical field intensity corresponding to each region. The processing device 130 can determine the physical field distribution based on the physical field intensity corresponding to each region.

[0059] Step 340: Determine the placement position of the nuclear magnetic resonance spectrometer based on the physical field distribution corresponding to at least two candidate placement areas. In some embodiments, step 340 may be performed by the processing device 130 or the placement position determination module 240.

[0060] In some embodiments, the processing device 130 can determine at least one target placement area from the candidate placement areas based on the physical field distribution corresponding to at least two candidate placement areas. Further, the processing device 130 can determine the placement position of the MRI scanner based on the at least one target placement area.

[0061] In some embodiments, for each of at least two candidate placement regions, the processing device 130 can determine the total physical field intensity of one or more other devices when the MRI scanner is operating in the candidate placement region, based on the physical field distribution corresponding to that candidate placement region. The processing device 130 can determine at least one target placement region based on the total physical field intensity corresponding to at least two candidate placement regions. In some embodiments, the processing device 130 can determine at least one target placement region using a placement region determination model based on the physical field distribution corresponding to at least two candidate placement regions. For a description of determining at least one target placement region, please refer to... Figure 4 Step 410 in the process will not be repeated here.

[0062] After determining at least one target placement area, the processing device 130 can determine the placement location of the MRI scanner based on that target placement area. For example, if there is only one target placement area, the processing device 130 can designate the center area of ​​the target placement area as the placement location of the MRI scanner. Alternatively, if there are at least two target placement areas, the processing device 130 can randomly designate or provide the user with a choice of target placement area as the final target placement area, and determine the placement location of the MRI scanner from the final target placement area.

[0063] In some embodiments, for each of at least one target placement area, the processing device 130 may determine at least two sub-regions of that target placement area. Optionally, the size of each sub-region is greater than or equal to the size of the MRI scanner. The processing device 130 may select one sub-region from the at least two sub-regions as the placement location for the MRI scanner. For a description of selecting one sub-region from at least two sub-regions as the placement location for the MRI scanner, please refer to [link to relevant documentation]. Figure 4 Step 440 in the previous section will not be repeated here.

[0064] In some embodiments, the processing device 130 can determine the placement position of the MRI scanner through one or more iterations based on the physical field distributions corresponding to at least two candidate placement areas. During each iteration, the processing device 130 can determine at least one target placement area from the at least two candidate placement areas based on the physical field distributions corresponding to the at least two candidate placement areas. For each of the at least one target placement area, the processing device 130 can determine at least two sub-regions of that target placement area. The processing device 130 can determine whether a termination condition is met. In response to the termination condition being met, the processing device 130 can select one sub-region from the at least two sub-regions as the placement position of the MRI scanner. In response to the termination condition not being met, the processing device 130 can designate the at least two sub-regions as at least two candidate placement areas in the next iteration. For a description of determining the placement position of the MRI scanner through one iteration or iteration, please refer to [link to relevant documentation]. Figure 4 The details and related descriptions will not be repeated here.

[0065] In some embodiments, after determining the placement of the MRI scanner, the processing device 130 may further determine whether other devices need to be repositioned, have an antimagnetic coating applied, or be placed in an antimagnetic cabinet. For example, for each other device, the processing device 130 may determine whether the placement of that device meets the requirements based on the physical field distribution in the scanning chamber when the MRI scanner is in that placement position and the maximum physical field intensity that the device can withstand. If, when the MRI scanner is in that placement position, the physical field intensity of the area occupied by a device is greater than the maximum physical field intensity that the device can withstand, the processing device 130 may determine that the placement of that device does not meet the requirements. In some embodiments, if the placement of another device does not meet the requirements, the processing device 130 may prompt the user to change the position of that device. For example, the processing device 130 may identify one or more candidate replacement positions in the scanning chamber where the physical field intensity is less than the maximum physical field intensity that the device can withstand, and send the determined replacement position to the user terminal to prompt the user to move the device to a candidate replacement position. In some embodiments, if the placement of other devices does not meet the requirements, the processing device 130 may prompt the user that the device needs to have an antimagnetic coating applied or be placed in an antimagnetic cabinet.

[0066] Figure 4 This is a schematic flowchart illustrating an exemplary method for determining the placement of an MRI scanner according to some embodiments of this specification. In some embodiments, this can be achieved through one or more steps of process 400. Figure 3 Step 340. (For example...) Figure 4 As shown, process 400 may include the following steps.

[0067] Step 410: Based on the physical field distribution corresponding to at least two candidate placement areas, determine at least one target placement area from the at least two candidate placement areas. In some embodiments, step 410 may be performed by the processing device 130 or the placement position determination module 240.

[0068] The candidate placement area refers to the area suitable for placing the MRI scanner. For further descriptions regarding the determination of candidate placement areas, please refer to other parts of the document, such as... Figure 3 Step 320 in the previous section will not be repeated here. The physical field distribution can display the distribution of the physical field in the scanning chamber during NMR scan operation, that is, the distribution pattern of the physical field intensity at different locations in the scanning chamber during NMR scan operation. For a description of the physical field distribution for determining the candidate placement area, please refer to other parts of the document, such as... Figure 3 Step 330 in the previous section will not be repeated here.

[0069] In some embodiments, for each of at least two candidate placement areas, the processing device 130 can determine the total magnetic field strength of one or more other devices based on the physical field distribution corresponding to that candidate placement area (i.e., the physical field distribution of the scanning chamber when the MRI scanner is operating in that candidate placement area). For example, for each other device, the processing device 130 can determine the average or maximum physical field strength corresponding to the area occupied by that device based on the physical field distribution, and specify the average or maximum physical field strength as the physical field strength corresponding to that device. In some embodiments, the processing device 130 can directly sum the physical field strengths corresponding to one or more other devices to determine the total magnetic field strength of one or more other devices when the MRI scanner is operating in that candidate placement area (also referred to as the total physical field strength corresponding to the candidate placement area). In some embodiments, the processing device 130 can perform a weighted summation of the physical field strengths corresponding to one or more other devices to determine the total physical field strength corresponding to the candidate placement area. For example, the processing device 130 can determine the importance of each other device based on its price, maintenance status, replacement status, service life, etc. The processing device 130 can determine a weighting coefficient for each other device based on its importance. For example, the weighting coefficient for one other device can be the importance of that other device divided by the sum of the importance of all other devices. Alternatively, the processing device 130 can determine the weighting coefficient based on the degree to which each other device is affected by the physical field. The more susceptible a device is to the physical field, the larger its weighting coefficient. The processing device performs a weighted summation based on the physical field strength of each other device and its corresponding weighting coefficient to determine the total physical field strength corresponding to the candidate placement area. By considering the importance of each other device and / or its degree of influence from the physical field to determine the total physical field strength of the candidate placement area, more effective protection can be provided for important devices and / or devices susceptible to the physical field, thereby reducing the impact or loss caused by strong physical fields.

[0070] The processing device 130 can determine at least one target placement area based on the total physical field strength corresponding to two candidate placement areas. For example, the processing device 130 can designate the candidate placement area with the smallest total magnetic field strength from at least two candidate placement areas as the target placement area. As another example, the processing device 130 can designate at least one candidate placement area with a total physical field strength less than a strength threshold as at least one target placement area. Furthermore, the processing device 130 can analyze whether the physical field strength corresponding to each other device exceeds its maximum tolerable physical field strength when the magnetic resonance imaging (MRI) scanner is placed in a specific candidate placement area. If the physical field strength corresponding to another device exceeds its maximum tolerable physical field strength, then that candidate placement area will not be used as a target placement area. If the physical field strength corresponding to all other devices does not exceed its maximum tolerable physical field strength, then that candidate placement area can be used as a target placement area.

[0071] In some embodiments, a user can view a three-dimensional virtual space of a scanning room through a virtual reality device. This three-dimensional virtual space may include a virtual scanning room corresponding to the scanning room, virtual devices corresponding to equipment placed inside the scanning room, and a virtual MRI scanner to be placed in the scanning room. In some embodiments, candidate placement areas may be marked in the virtual scanning room. The user can perform virtual placement operations in the three-dimensional virtual space presented by the virtual reality device through control components to determine at least one target placement area. As an example only, the user can control the virtual MRI scanner in the three-dimensional virtual space to be placed in one or more suitable candidate placement areas by operating the control components. The virtual reality device may display the total physical field intensity corresponding to the candidate placement area and / or the ranking of the total physical field intensity of the candidate placement areas for which the user has performed virtual placement operations. After virtual placement operations have been performed on all candidate placement areas selected by the user, at least one candidate placement area with the lowest physical field intensity can be selected as the target placement area based on the ranking of the physical field intensity of each candidate placement area. In this way, the selection of the target placement can be visualized, making it easier for the user to quickly locate an area suitable for placing the MRI scanner.

[0072] In some embodiments, candidate placement areas can be displayed differently from other areas. In some embodiments, areas where the user has already performed virtual placement operations can be displayed differently from other candidate placement areas where virtual placement operations are yet to be performed, thereby helping the user to quickly perform subsequent virtual placement operations and improving the efficiency of determining the target placement area.

[0073] In some embodiments, the processing device 130 can determine at least one target placement area based on the physical field distributions corresponding to at least two candidate placement areas using a placement area determination model. The placement area determination model can be a model used to determine the target placement area for an MRI scanner. As an example only, the processing device 130 can input the physical field distributions corresponding to at least two candidate placement areas and reference information from one or more other devices into the placement area determination model. The reference information from the one or more other devices can at least include the maximum physical field intensity that each other device can withstand. In some embodiments, the reference information from the one or more other devices can also include other information about each other device, such as the relative distance between each other device and the candidate placement area, the distance relative to the wall, and the position of each other device in the scanning chamber. The placement area determination model can output information related to each candidate placement area. For example, the placement area determination model can directly output at least one target placement area. Alternatively, the placement area determination model can output a score for each candidate placement area, which reflects the suitability of placing the MRI scanner in that candidate placement area. The processing device 130 can determine the target placement area based on the output of the placement area determination model. For example, a higher score for a candidate placement area indicates that the candidate placement area is more suitable for placing the MRI scanner. The processing device 130 can select at least one candidate placement area with a score exceeding a score threshold as the target placement area.

[0074] In some embodiments, the placement region determination model may include Convolutional Neural Networks (CNN), Residual Networks (ResNet), Deep Reinforcement Learning Algorithms (DRLA), Genetic Algorithms (GA), etc. In some embodiments, the processing device 130 can... Figure 1 One or more components of application scenario 100 (e.g., the storage device in application scenario 100) or external devices acquire the placement area determination model. For example, the placement area determination model may be pre-trained and generated by a computing device (e.g., processing device 130) and stored in the storage device of application scenario 100. Processing device 130 may access the storage device and retrieve the placement area determination model.

[0075] In some embodiments, a placement area determination model can be obtained by training an initial model based on multiple training samples. As an example only, each training sample may include the physical field distribution of at least two candidate placement areas in the sample scanning chamber, sample reference information from one or more other devices, and sample labels. Sample labels may include whether each candidate placement area can be used as a target placement area and / or a score for each candidate placement area. Sample labels can be manually calibrated or confirmed as training ground values. Training the initial model may include one or more iterations, each iteration including updating the model parameters based on the training samples. In some embodiments, the optimization objective of the initial model training may include adjusting the model parameters to reduce the value of the loss function (e.g., minimizing the value of the loss function). The loss function can be used to characterize the difference between the output of the initial model and the ground values ​​of the sample labels. Exemplarily, the loss function may include a focus loss function, a log loss function, a cross-entropy loss, etc. For example, the physical field distribution of at least two candidate placement areas in each training sample, and sample reference information from one or more other devices can be input into the initial model, which can output at least one predicted target placement area and / or a predicted score for each candidate placement area. The loss function can be used to characterize the difference between the predicted values ​​of the initial model output and the true values ​​of the sample labels.

[0076] In some embodiments, training can stop when the initial model meets the training termination condition in a certain iteration. Exemplary training termination conditions may include any one or a combination of the following: the value of the loss function obtained in a certain iteration is less than a threshold, a certain number of iterations have been performed, or the loss function has converged (e.g., the difference between the value of the loss function obtained in the previous iteration and the value of the loss function obtained in the current iteration is within a preset threshold). In some embodiments, if the training termination condition is not met in an iteration, the processing device 130 may further update the initial model for the next iteration according to a preset algorithm (e.g., backpropagation algorithm). If the training termination condition is met in the current iteration, the processing device 130 can complete the training of the initial model, and the trained initial model can be used as a model to determine the placement area.

[0077] Step 420: For each of the at least one target placement area, determine at least two sub-areas of that target placement area. In some embodiments, step 420 may be performed by the processing device 130 or the placement position determination module 240.

[0078] The size of each sub-region is greater than or equal to the size of the MRI scanner. In some embodiments, different sub-regions may be the same size. In some embodiments, at least two of the at least two sub-regions in each target placement area may have overlapping areas. For example, two adjacent sub-regions in at least two sub-regions may have partial overlap. As another example, multiple consecutive sub-regions in at least two sub-regions may have partial overlap.

[0079] Step 430: Determine whether the termination condition is met. In some embodiments, step 430 may be performed by the processing device 130 or the placement determination module 240.

[0080] Termination conditions may include any one or a combination of the following: a certain number of iterations have been performed, the total physical field intensity corresponding to at least one sub-region is less than an intensity threshold, the difference between the minimum total physical field intensity corresponding to the sub-region in the current iteration and the previous iteration is less than a threshold, the size of all sub-regions is less than a size threshold, etc. In response to determining that the termination conditions are not met, processing device 130 executes step 450. In response to determining that the termination conditions are met, processing device 130 executes step 440.

[0081] Step 440: Select one of at least two sub-regions as the placement location for the MRI scanner. In some embodiments, step 440 may be performed by the processing device 130 or the placement location determination module 240.

[0082] In some embodiments, the processing device 130 can arbitrarily select one sub-region from at least two sub-regions as the placement location of the MRI scanner. In some embodiments, the processing device 130 can determine the physical field distribution in the scanning chamber when the MRI scanner is operating in each sub-region. The processing device 130 can determine the total physical field intensity corresponding to each sub-region based on the determined physical field distribution. The processing device 130 can select the sub-region with the minimum total physical field intensity as the placement location of the MRI scanner. In some embodiments, the processing device 130 can determine the placement location of the MRI scanner based on factors such as the importance of other equipment. For example, the processing device can identify one or more important equipment based on the importance of each other equipment. The processing device 130 can select the sub-region farthest from the important equipment as the placement location of the MRI scanner. Alternatively, the processing device 130 can select a sub-region that meets the usage requirements of the important equipment as the placement location of the MRI scanner.

[0083] Step 450: Designate at least two sub-regions as at least two candidate placement regions for the next iteration. In some embodiments, step 450 may be performed by processing device 130 or placement position determination module 240.

[0084] Processing device 130 can designate at least two sub-regions as at least two candidate placement regions in the next iteration, re-execute steps 410-430 until the termination condition is met, and determine the placement position of the nuclear magnetic resonance spectrometer according to step 440.

[0085] According to some embodiments of this specification, the processing device 130 can first eliminate areas that are unsuitable or unsuitable for placing an MRI scanner to determine at least two candidate placement areas, and then determine at least one target placement area from the at least two candidate placement areas. Subsequently, sub-regions of each target placement area are analyzed through at least one iterative operation. In this way, unusable areas can be quickly and easily eliminated, reducing subsequent computational load. The size of the target placement area can be gradually reduced through the iterative process; that is, the placement location of the MRI scanner can be roughly determined first, and then gradually refined to determine the placement location of the MRI scanner more accurately. Compared with directly analyzing which area in each small region of the scanning chamber is suitable for placing an MRI scanner, this specification can significantly reduce the amount of data processing (e.g., the amount of data input to the model can be greatly reduced), thereby improving the efficiency of determining the placement location of the MRI scanner and saving computational resources.

[0086] In some embodiments of this specification, the placement position of the MRI scanner in the scanning room is determined based on the physical field distribution corresponding to at least two candidate placement areas in the scanning room. The beneficial effects of these embodiments include, but are not limited to: (1) obtaining a relatively accurate placement position of the MRI scanner based on the physical field distribution corresponding to at least two candidate placement areas in the scanning room, thereby reducing the influence of the physical field on other equipment in the scanning room; (2) using a placement area determination model to determine the target placement area of ​​the MRI scanner, which can reduce user workload and manual intervention, thereby improving the accuracy and efficiency of determining the target placement area; (3) based on reference data of the scanning room, excluding areas in the three-dimensional spatial model unsuitable for placing the MRI scanner, and further dividing the remaining suitable areas into blocks, can ensure that all obtained blocks are areas suitable for placing the MRI scanner, while reducing the number of blocks. (4) Based on the reference position of the reference MRI in the reference scanning chamber, the key areas in the three-dimensional spatial model are densely divided into blocks, which can improve the accuracy of the candidate placement areas. (5) Compared with directly analyzing which area in each small area of ​​the scanning chamber is suitable for placing the MRI, this specification can greatly reduce the amount of data processing (e.g., the amount of data in the input model can be greatly reduced) by first eliminating unusable areas and then gradually reducing the size of the target placement area through an iterative process. This can improve the efficiency of determining the placement position of the MRI and save computing resources. (6) By considering the importance of each other device and / or the degree of influence of the physical field, the total intensity of the physical field of the candidate placement area is determined, which can more effectively protect important devices and / or devices that are susceptible to the physical field, thereby reducing the impact or loss caused by strong physical fields. (7) By using virtual reality devices to determine the target placement area, the selection of the target placement area can be visualized, making it easy for users to quickly locate the area suitable for placing the MRI. (8) Using virtual reality devices to display different areas can improve the efficiency of determining the target placement area.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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 of determining a set-up position of a nuclear magnetic resonance instrument, performed by at least one processor, characterized by, The method includes: Obtain a three-dimensional spatial model of the scanning chamber for placing the nuclear magnetic resonance spectrometer, the scanning chamber including one or more other devices placed therein; Based on the three-dimensional spatial model, at least two candidate placement areas for the nuclear magnetic resonance spectrometer are determined; For each of the at least two candidate placement areas, determine the physical field distribution of the scanning chamber when the MRI scanner is running in the candidate placement area, the physical field distribution showing the distribution pattern of physical field intensity at different locations in the scanning chamber when the MRI scanner is running; For each of the at least two candidate placement regions, Based on the importance of each of the other devices and the degree to which they are affected by the physical field, a weighting coefficient is determined for each of the other devices; and The total physical field intensity of the other devices corresponding to the candidate placement area is determined by weighted summation based on the physical field intensity of each of the other devices and the corresponding weighting coefficient. Based on the total physical field intensity of the other devices corresponding to the at least two candidate placement areas, at least one target placement area is determined; and The placement position of the nuclear magnetic resonance spectrometer is determined based on the at least one target placement area.

2. The method of claim 1, wherein, Determining the placement of the MRI scanner based on the at least one target placement area includes one or more iterations, wherein each iteration in at least one iteration includes: For each of the at least one target placement area, at least two sub-areas of that target placement area are determined; Determine whether the termination condition is met; and In response to the termination condition being met, one of the at least two sub-regions is selected as the placement location of the MRI scanner; or in response to the termination condition not being met, the at least two sub-regions are designated as at least two candidate placement regions in the next iteration.

3. The method of claim 1, wherein, Determining at least one target placement area based on the total physical field intensity of the other devices corresponding to the at least two candidate placement areas includes: Based on the total physical field intensity of the other devices corresponding to the at least two candidate placement areas and the reference information of the one or more other devices, the at least one target placement area is determined using a placement area determination model. The reference information of the one or more other devices includes at least the maximum physical field intensity that each of the other devices can withstand.

4. The method of claim 1, wherein, The determination of at least two candidate placement regions for the nuclear magnetic resonance spectrometer based on the three-dimensional spatial model includes: Acquire reference data for the scanning chamber, the reference data being at least related to the one or more other placed devices; Based on the reference data, the exclusionary regions in the three-dimensional spatial model are determined; Based on the excluded region, at least two blocks are determined from at least a portion of the three-dimensional spatial model; and From the at least two blocks, determine the at least two candidate placement areas.

5. The method as described in claim 4, characterized in that, The reference data includes data relating to the one or more other devices and data relating to the user’s range of movement in the scanning room.

6. The method of claim 4, wherein, The step of determining at least two blocks from at least a portion of the three-dimensional spatial model based on the excluded region includes: Mapping the exclusion zone onto the ground to determine the exclusion zone on the ground; and The portion of the ground surface excluding the excluded area is divided into blocks to determine the at least two blocks.

7. The method of claim 4, wherein, The step of determining at least two blocks from at least a portion of the three-dimensional spatial model based on the excluded region includes: Obtain the reference position of the reference MRI scanner in the reference scanning chamber; and Based on the reference position of the reference MRI scanner in the reference scanning chamber, the at least two blocks are determined from the portion of the three-dimensional spatial model excluding the excluded region.

8. A system for determining the placement of an nuclear magnetic resonance (NMR) spectrometer, comprising: The acquisition module is used to acquire a three-dimensional spatial model of the scanning chamber for placing the nuclear magnetic resonance spectrometer, the scanning chamber including one or more other devices placed therein; The placement area determination module is used to determine at least two candidate placement areas for the nuclear magnetic resonance spectrometer based on the three-dimensional spatial model. A physical field distribution determination module is used to determine, for each of the at least two candidate placement areas, the physical field distribution of the scanning chamber when the MRI scanner is operating in the candidate placement area, wherein the physical field distribution shows the distribution pattern of the physical field intensity at different locations in the scanning chamber when the MRI scanner is operating; and The placement determination module is used for: For each of the at least two candidate placement regions, The weighting coefficients for each of the other devices are determined based on their importance and the degree to which they are affected by the physical field. as well as The total physical field intensity of the other devices corresponding to the candidate placement area is determined by weighted summation based on the physical field intensity of each of the other devices and the corresponding weighting coefficient. Based on the total physical field intensity of the other devices corresponding to the at least two candidate placement areas, at least one target placement area is determined; as well as The placement position of the nuclear magnetic resonance spectrometer is determined based on the at least one target placement area.

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