A wiring method and system

Abstract

The embodiment of the specification discloses a wiring method and system. The wiring method comprises the following steps: acquiring physical field distribution information of a nuclear magnetic resonance room to be wired, constructing a graph structure model based on the physical field distribution information, and determining a wiring scheme of the nuclear magnetic resonance room based on the graph structure model.

Description

Technical Field

[0001] This specification relates to the field of cable routing, and in particular to a wiring method and system for use in a nuclear magnetic resonance imaging (MRI) room. Background Technology

[0002] Magnetic Resonance Imaging (MRI) is widely used in medical imaging diagnosis. Because MRI scanners generate high-intensity physical fields (e.g., magnetic fields, electromagnetic fields) during operation, and cables are susceptible to the effects of these fields, the physical field distribution along the wiring path must be considered when laying cables in an MRI room, and an assessment must be made as to whether additional magnetic shielding measures are necessary. Typically, manually designing wiring schemes often involves complex calculations, low efficiency, and unsatisfactory wiring results due to the need to consider the physical field distribution within the MRI room.

[0003] Therefore, it is desirable to provide a method and system for efficiently and accurately determining the wiring scheme of an MRI room. Summary of the Invention

[0004] One embodiment of this specification provides a wiring method. The wiring method includes: acquiring physical field distribution information of a nuclear magnetic resonance (NMR) chamber to be wired; constructing a graph structure model based on the physical field distribution information; and determining a wiring scheme for the NMR chamber based on the graph structure model.

[0005] In some embodiments, obtaining the physical field distribution information of the nuclear magnetic resonance chamber to be wired includes: predicting the physical field distribution information in the nuclear magnetic resonance chamber when the nuclear magnetic resonance instrument is running, based on the analysis of relevant data of the nuclear magnetic resonance instrument and the nuclear magnetic resonance chamber.

[0006] In some embodiments, obtaining the physical field distribution information of the MRI chamber to be wired includes: obtaining a three-dimensional spatial model of the MRI chamber; obtaining feature information of the MRI scanner, the feature information including at least the physical field emission model of the MRI scanner; and determining the physical field distribution information based on the three-dimensional spatial model and the feature information using a physical field simulation model.

[0007] In some embodiments, constructing a graph structure model based on the physical field distribution information includes: constructing a house grid of the MRI room, the house grid including at least two candidate nodes; determining at least two target nodes from the at least two candidate nodes; determining at least one edge connecting the at least two target nodes based on the physical field distribution information; and constructing the graph structure model based on the at least two target nodes and the at least one edge.

[0008] In some embodiments, the graph structure model includes a plurality of target nodes and a plurality of corresponding edges.

[0009] In some embodiments, determining at least two target nodes from the at least two candidate nodes includes: determining an exclusion region of the MRI chamber; and determining the candidate nodes outside the exclusion region as target nodes.

[0010] In some embodiments, determining at least two target nodes from the at least two candidate nodes includes: for each candidate node, determining whether the physical field intensity at the location in the MRI chamber corresponding to the candidate node exceeds a predetermined physical field intensity threshold; and determining the candidate node as the target node in response to the physical field intensity at the location in the MRI chamber corresponding to the candidate node being less than the physical field intensity threshold.

[0011] In some embodiments, constructing the graph structure model based on the physical field distribution information further includes: for each of the at least one edge, determining the physical field strength of the target node corresponding to the edge based on the physical field distribution information; and determining the weight of the edge based on the physical field strength of the target node corresponding to the edge.

[0012] In some embodiments, determining the weight of an edge based on the magnetic field strength of the target node corresponding to the edge further includes: determining the probability of placing an item at the location of the target node corresponding to the edge; and determining the weight of the edge based on the magnetic field strength and the probability.

[0013] In some embodiments, determining the weight of an edge based on the magnetic field strength of the target node corresponding to the edge further includes: determining an initial weight of the edge based on the physical field strength of the target node corresponding to the edge; and adjusting the initial weight based on the material cost of the selected cable to determine the weight of the edge.

[0014] In some embodiments, determining the wiring scheme of the MRI room based on the graph structure model includes: determining a first node corresponding to the starting point of the wiring and a second node corresponding to the ending point of the wiring in the graph structure model; and determining at least one wiring path connecting the first node and the second node based on the weight of each edge in the graph structure model, wherein the weight of the edges on each wiring path satisfies a preset condition.

[0015] In some embodiments, determining at least one routing path connecting the first node and the second node based on the weight of each edge in the graph structure model includes: determining at least one routing path connecting the first node and the second node based on the shortest path algorithm and the graph structure model.

[0016] In some embodiments, the shortest path algorithm includes the Bellman-Ford algorithm and the Dixlat algorithm.

[0017] In some embodiments, the at least one wiring path includes at least two wiring paths, and determining the wiring scheme of the MRI room based on the graph structure model further includes: determining a target wiring path from the at least two wiring paths based on the lengths of the at least two wiring paths.

[0018] In some embodiments, the preset conditions include the weights of the edges on the wiring path and the weight threshold.

[0019] In some embodiments, the preset condition includes the total length of the wiring path being less than a length threshold.

[0020] In some embodiments, determining the wiring scheme of the MRI room based on the graph structure model further includes: if there is no wiring path that meets the preset conditions, updating the weight of the at least one edge based on the antimagnetic measures; and determining the at least one wiring path connecting the first node and the second node based on the updated weight of the at least one edge.

[0021] In some embodiments, the method further includes presenting the wiring scheme to the wearer of the virtual reality device via a virtual reality device.

[0022] In some embodiments, the method further includes: generating a virtual reality model based on the physical field distribution information of the MRI chamber and the wiring scheme; and presenting the virtual reality model using a virtual reality device.

[0023] In some embodiments, the virtual reality device includes a control component for manipulating the virtual reality model.

[0024] In some embodiments, if the at least one wiring path includes at least two wiring paths, the method further includes: presenting both of the at least two wiring paths to the wearer of the virtual reality device via a virtual reality device; and receiving a target wiring path determined by the wearer of the virtual reality device from the at least two wiring paths.

[0025] In some embodiments, the physical field includes at least one of a magnetic field and an electromagnetic field.

[0026] One embodiment of this specification provides a cabling system, the cabling system comprising: at least one storage device for storing computer instructions; and at least one processor configured to communicate with the at least one storage device, wherein, when executing the set of instructions, the at least one processor is configured to perform the following operations: acquiring physical field distribution information of an MRI room to be cabled; constructing a graph structure model based on the physical field distribution information; and determining a cabling scheme for the MRI room based on the graph structure model.

[0027] One embodiment of this specification provides a wiring system, the wiring system comprising: an acquisition module for acquiring physical field distribution information of a nuclear magnetic resonance chamber to be wired; a model building module for constructing a graph structure model based on the physical field distribution information; and a determination module for determining a wiring scheme for the nuclear magnetic resonance chamber based on the graph structure model. Attached Figure Description

[0028] 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:

[0029] Figure 1 These are schematic diagrams illustrating application scenarios of cabling systems according to some embodiments of this specification;

[0030] Figure 2 This is a block diagram of a cabling system according to some embodiments of this specification;

[0031] Figure 3 This is an exemplary flowchart of a wiring method according to some embodiments of this specification;

[0032] Figure 4 This is an exemplary flowchart of a construction graph structure model shown in some embodiments of this specification;

[0033] Figure 5 This is an exemplary flowchart illustrating the determination of a wiring scheme according to some embodiments of this specification; and

[0034] Figure 6 This is a schematic diagram illustrating the determination of wiring paths according to some embodiments of this specification. Detailed Implementation

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] This specification provides wiring methods and systems for cable layout in spaces influenced by physical fields (e.g., spaces where physical field generating devices are fixedly placed). For illustrative purposes, this specification uses an MRI room as an example. It should be understood that the wiring methods and systems disclosed in this specification can also be used in other spaces where physical fields exist. 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) around it. The physical field in this application includes at least one of magnetic and electromagnetic fields.

[0040] Some embodiments of this specification provide a wiring method and system that acquires the physical field distribution information of the MRI room to be wired, constructs a graph structure model based on the physical field distribution information, and finally determines the wiring scheme of the MRI room based on the graph structure model. In some embodiments, the wiring method and system can also generate a virtual reality model corresponding to the determined wiring scheme and present it to the wiring operator through a virtual reality device. By constructing a graph structure model, a suitable wiring path can be quickly calculated, assisting the wiring operator in determining the wiring path. At the same time, the virtual reality device can also intuitively display the wiring scheme, effectively improving wiring efficiency.

[0041] Figure 1 These are schematic diagrams illustrating application scenarios of the cabling system 100 according to some embodiments of this specification. For example... Figure 1 As shown, the application scenarios of the cabling system 100 may include a processing device 110, an MRI room 120, and a virtual reality device 130. In some embodiments, the cabling system 100 may also include a network and a storage device (not shown). In some embodiments, two or more components of the cabling system 100 may be connected to and / or communicate with each other via a wireless connection (e.g., a network), a wired connection, or a combination thereof. The connections between the components of the cabling system 100 may be variable. By way of example only, the virtual reality device 130 may be connected to the processing device 110 via a network or directly. As another example, the storage device may be connected to the processing device 110 via a network or directly.

[0042] The MRI room 120 can be used to house an MRI scanner 121. The MRI scanner can be any medical device that utilizes the magnetic resonance phenomenon. In some embodiments, the MRI scanner 121 may include an 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 MRI room 120 may also be used to house other electronic devices, such as computing devices, control devices, display devices, etc.

[0043] To meet the operational requirements of the MRI scanner 121 and other electronic equipment in the MRI chamber 120, it is necessary to determine the cabling scheme within the MRI chamber 120. The cable in this application is a power or signal transmission device used to connect electronic equipment and is typically composed of wires. In some embodiments, the cable may include power cables, control cables, compensating cables, shielded cables, high-temperature cables, computer cables, signal cables, coaxial cables, fire-resistant cables, marine cables, mining cables, aluminum alloy cables, etc. Determining the cabling scheme may include determining one or more of the following: the cabling start point, the cabling end point, the cabling path between the start and end points for each electronic device, the cable material used, and whether anti-magnetic measures are required on the cable. This is merely an example. Figure 1 The diagram shows the wiring start point 122 and wiring end point 123 of the MRI scanner 121. Wiring start point 122 is the starting point of the proposed wiring scheme, and wiring end point 123 is the ending point. Wiring start point 122 and wiring end point 123 can be set by the wiring engineer or determined based on the spatial layout of the MRI room 120. Cables can connect wiring start point 122 and wiring end point 123 along wiring path 124. Wiring start point 122 can be a power port, network port, etc., of the MRI room 120. Wiring end point 123 can be a port (such as a socket) providing power or network access to the MRI scanner 121. Because the MRI scanner 121 can generate a physical field in the surrounding space during operation, this physical field may affect the cables arranged within the MRI room 120; the influence of this physical field needs to be considered when designing the wiring scheme.

[0044] Processing device 110 can process data and / or information related to cabling system 100. In some embodiments, processing device 110 can determine the cabling scheme of MRI chamber 120 based on the physical field distribution information of MRI chamber 120. For example, such as Figure 1As shown, processing device 110 can determine a wiring path 124 between wiring start point 122 and wiring end point 123. In some embodiments, the MRI scanner 121 and / or other equipment have been placed in the MRI chamber 120, and processing device 110 can determine or adjust the wiring scheme of the MRI chamber based on relevant information of the placed equipment. In some embodiments, the MRI scanner 121 and other equipment have not yet been placed in the MRI chamber 120, and processing device 110 can determine the wiring scheme of the MRI chamber based on information about the equipment to be placed in the MRI chamber 120. In some embodiments, processing device 110 can be a server or a server group. The server group can be centralized or distributed. In some embodiments, processing device 110 can be local or remote. For example, processing device 110 can access information and / or data stored in virtual reality device 130 and / or storage device via a network. For example, processing device 110 can directly connect to virtual reality device 130 and / or storage device to access the information and / or data stored therein. In some embodiments, processing device 110 can be executed on a cloud platform. For example, a cloud platform may include one or more combinations of private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, interconnected cloud, multi-cloud, etc. In some embodiments, the processing device 110 may be executed by a computing device having one or more components. In some embodiments, the processing device 110 may be integrated into the MRI scanner 121, or the MRI scanner 121 and the processing device 110 may function through the same entity. The medical devices described above are for illustrative purposes only and are not intended to limit the scope of this specification.

[0045] The virtual reality device 130 enables user interaction with the processing device 110, the MRI scanner 121, and / or other devices. For example, the virtual reality device 130 can be used to present a virtual reality model, which can be generated based on a defined wiring scheme. In some embodiments, the virtual reality device 130 is typically worn by the user, who can view the virtual reality model through the device to intuitively and clearly understand the wiring scheme. For example, the user can view each segment of the wiring path 124 and its corresponding surroundings (e.g., physical field distribution) through the device 130. Optionally, the user can also adjust the wiring path using the virtual reality device 130. For example, the virtual reality device 130 is equipped with control components such as a controller, motion capture system, gloves, or a stylus. The user can use these control components to adjust the wiring path 124. In some embodiments, if the wiring path determined by the wiring system 100 includes at least two paths, the virtual reality device 130 can present at least two wiring paths to the user, who can then select the final wiring path to implement. In some embodiments, a virtual reality device may include one or more combinations of virtual reality helmets, virtual reality glasses, virtual reality goggles, augmented reality helmets, augmented reality glasses, augmented reality goggles, etc. For example, a virtual reality device and / or an augmented reality device may include Google Glass. TM Oculus Rift TM HoloLens TM Gear VR TM wait.

[0046] The network may include any suitable network for exchanging information and / or data within the cabling system 100. In some embodiments, one or more components of the cabling system 100 (e.g., processing device 110, MRI scanner 121, virtual reality device 130, storage device, etc.) may communicate information and / or data with one or more other components of the cabling system 100 via the network. For example, processing device 110 may obtain information about the physical field distribution of the MRI chamber 120 from a storage device via the network.

[0047] Networks can be and / or include public networks (e.g., the Internet), private networks (e.g., local area networks (LANs), wide area networks (WANs)), wired networks (e.g., Ethernet networks), wireless networks (e.g., 802.11 networks, Wi-Fi networks), cellular networks (e.g., Long Term Evolution (LTE) networks), Frame Relay networks, virtual private networks (“VPNs”), satellite networks, telephone networks, routers, hubs, switches, server computers, and / or any combination thereof. By way of example only, networks can include cable networks, wired networks, fiber optic networks, telecommunications networks, intranets, wireless local area networks (WLANs), metropolitan area networks (MANs), public switched telephone networks (PSTNs), Bluetooth, etc. TM Network, Purple Bee TM A network, a near-field communication (NFC) network, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include wired and / or wireless network access points such as base stations and / or internet exchange points, through which one or more components of the cabling system 100 can connect to the network to exchange data and / or information.

[0048] The storage device can store data, instructions, and / or any other information. In some embodiments, the storage device can store data obtained from the MRI scanner 121, the virtual reality device 130, and / or the processing device 110. In some embodiments, the storage device can store data and / or instructions, and the processing device 110 can execute or use the data and instructions to perform the exemplary methods described in this application. In some embodiments, the storage device may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), etc., or any combination thereof. Exemplary mass storage may include a hard disk, an optical disk, a solid-state drive, etc. Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a compact disk, a magnetic tape, etc. Exemplary volatile read-write memory may include random access memory (RAM). Exemplary RAM may include dynamic random access memory (DRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), static random access memory (SRAM), thyristor random access memory (T-RAM), and zero-capacitance random access memory (Z-RAM), etc. Exemplary ROMs may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), optical disc read-only memory (CD-ROM), and digital multifunction disk read-only memory, etc. In some embodiments, the storage device may operate on a cloud platform. By way of example only, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-tiered cloud, etc., or any combination thereof.

[0049] In some embodiments, the storage device may be connected to a network to communicate with one or more other components of the cabling system 100 (e.g., processing device 110, MRI scanner 121, virtual reality device 130). One or more components of the cabling system 100 may access data or instructions stored in the storage device via the network. In some embodiments, the storage device may be directly connected to or communicate with one or more other components of the cabling system 100 (e.g., processing device 110, MRI scanner 121, virtual reality device 130). In some embodiments, the storage device may be part of the processing device 110.

[0050] Figure 2 This is a block diagram of a cabling system 200 according to some embodiments of this specification. For example... Figure 2 As shown, the cabling system 200 may include an acquisition module 210, a model building module 220, and a determination module 230.

[0051] The acquisition module 210 can be used to acquire the physical field distribution information of the MRI chamber to be wired. In some embodiments, the acquisition module 210 can acquire the physical field distribution information of the MRI chamber stored in a storage device. In some embodiments, the acquisition module 210 can acquire a three-dimensional spatial model of the MRI chamber and the feature information of the MRI scanner. In some embodiments, the acquisition module 210 can determine the physical field distribution information based on the three-dimensional spatial model and feature information using a physical field simulation model. Further description of the acquisition module 210 can be found elsewhere in this specification (e.g., the description of step 310).

[0052] The model building module 220 can be used to build a graph structure model based on physical field distribution information. In some embodiments, the model building module 220 can build a house grid of an MRI chamber, the house grid including at least two candidate nodes. In some embodiments, the model building module 220 can determine at least two target nodes from at least two candidate nodes. In some embodiments, the model building module 220 can determine at least one edge connecting the at least two target nodes based on physical field distribution information. In some embodiments, the model building module 220 can construct a graph structure model based on the at least two target nodes and the at least one edge. In some embodiments, the model building module 220 can determine the physical field strength of the target node corresponding to each of the at least one edge based on physical field distribution information, and determine the weight of the edge based on the physical field strength of the target node corresponding to the edge. Further description of the model building module 220 can be found elsewhere in this specification (e.g., step 320 and the specification). Figure 4 (Related instructions).

[0053] The determining module 230 can be used to determine the wiring scheme of the MRI room based on a graph structure model. In some embodiments, the determining module 230 can determine a first node corresponding to the wiring start point and a second node corresponding to the wiring end point in the graph structure model. In some embodiments, the determining module 230 can determine at least one wiring path connecting the first node and the second node based on the weight of each edge in the graph structure model, wherein the weights of the edges on each wiring path satisfy a preset condition. In some embodiments, if the at least one wiring path includes at least two wiring paths, the determining module 230 can determine a target wiring path from the at least two wiring paths based on the lengths of the at least two wiring paths. In some embodiments, if there is no at least one wiring path that satisfies the preset condition, the determining module 230 can update the weight of the at least one edge based on antimagnetic measures, and determine at least one wiring path connecting the first node and the second node based on the updated weight of the at least one edge. Further description of the determining module 230 can be found elsewhere in this specification (e.g., step 330 and the specification). Figure 5 (Related instructions).

[0054] It should be understood that Figure 2 The system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of both.

[0055] It should be noted that the above description of the system and its modules is for convenience only and should not be construed as limiting this specification to the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the modules or construct subsystems connected to other modules without departing from these principles. For example, in some embodiments, for instance, Figure 2 The acquisition module 210, model building module 220, and determination module 230 disclosed herein can be different modules within a single system, or a single module can implement the functions of two or more of the aforementioned modules. 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.

[0056] Figure 3This is an exemplary flowchart of a wiring method according to some embodiments of this specification. Flow 300 can be executed by a processing device (e.g., processing device 110). For example, flow 300 can be implemented as an instruction set (e.g., an application program) stored in memory internal or external to the wiring system 100. The processing device can execute the instruction set and, when executing the instructions, can be configured to execute flow 300. The operational schematic diagram of flow 300 presented below is illustrative. In some embodiments, the process can be accomplished using one or more additional operations not described and / or by omitting one or more operations discussed below. Additionally, Figure 3 The order of operations shown in and described below in process 300 is not intended to be restrictive.

[0057] Step 310: Obtain the physical field distribution information of the nuclear magnetic resonance chamber to be wired. In some embodiments, step 310 may be performed by the processing device 110 or the acquisition module 210.

[0058] An MRI room can house an MRI scanner and other equipment, such as computing devices, control devices, and display devices. Because the MRI scanner generates a physical field in the surrounding space during operation, this field can affect the cabling within the MRI room. Therefore, when designing the cabling scheme for the MRI room, the distribution of the physical field needs to be considered to minimize its impact on the wiring. In some embodiments, the MRI scanner may include an MRI scanner, an X-ray imaging-MRI scanner, a single-photon emission computed tomography-MRI scanner, or a digital subtraction angiography-MRI scanner.

[0059] The physical field distribution information reflects the distribution pattern of physical field intensity at different locations within the MRI chamber during the operation of the MRI scanner. The MRI scanner radiates physical fields in all directions during operation; these fields may be reflected or absorbed by other equipment within the MRI chamber, forming the final physical field distribution. In some embodiments, the MRI scanner and other equipment are already placed in the MRI chamber, and the physical field distribution can be determined based on their placement. In some embodiments, the MRI scanner and other equipment are not yet placed in the MRI chamber, and the physical field distribution can be determined based on their intended placement. In some embodiments, the physical field distribution information can be represented by a three-dimensional model containing physical field intensity information. In some embodiments, the processing device can retrieve the physical field distribution information of the MRI chamber stored in a storage device.

[0060] In some embodiments, if an MRI scanner is installed in an MRI chamber, the scanner can perform scans within the chamber. Physical field strength measuring devices can be installed in multiple areas within the MRI chamber to measure the physical field strength. Based on the physical field strength measurements taken by the physical field strength measuring devices at various locations within the MRI chamber, the processing equipment can determine the physical field distribution information of the MRI chamber.

[0061] In some embodiments, the processing device can predict the physical field distribution information in the MRI chamber during MRI operation based on the analysis of data related to the MRI scanner and MRI chamber. For example, the physical field distribution in the MRI chamber during MRI operation can be determined by the processing device based on existing physical field simulation algorithms. In some embodiments, the physical field distribution in the MRI chamber during MRI operation can be determined based on the Finite Element Analysis (FEA) algorithm. Finite Element Analysis-based physical field simulation mainly decomposes the 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.

[0062] For example, the processing device can acquire a three-dimensional spatial model of the MRI chamber and the feature information of the MRI scanner. Based on the three-dimensional spatial model and feature information, the processing device can determine the physical field distribution information using a physical field simulation model. The three-dimensional spatial model of the MRI chamber can be a three-dimensional model representing the internal scene of the MRI chamber. In some embodiments, the three-dimensional spatial model of the MRI chamber can be used to represent the internal spatial structure of the MRI chamber and one or more objects located inside the MRI chamber. One or more objects located inside the MRI chamber can include the MRI scanner, cabinet, computing device, control device, tables and chairs, walls, floor, etc. In some embodiments, the three-dimensional spatial model of the MRI chamber can be constructed based on point cloud data, depth data, etc. of the MRI chamber. For example, point cloud data can be acquired by sensors (e.g., LiDAR). Depth data can be acquired by depth cameras. In some embodiments, the three-dimensional spatial model of the MRI chamber can be reconstructed from multiple two-dimensional images using three-dimensional reconstruction technology.

[0063] The characteristic information of an MRI scanner may include its physical field emission model, location, size, weight, etc. 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 field emitted by the scanner at different distances during operation. In some embodiments, the processing device can determine the physical field emission model of the MRI scanner based on its performance parameters (e.g., the physical field intensity of the main magnet), scanning parameters (e.g., scanning protocol), etc. In some embodiments, the processing device can determine the physical field emission model based on the performance parameters of one or more components of the MRI scanner used to emit magnetic and / or electromagnetic fields. For example, the processing device can acquire the magnetic field intensity emitted by the main magnet to different locations during MRI scanner operation. For each location in the scanning chamber, the processing device can use the magnetic field intensity at that location as the physical field intensity at that location, thereby determining the physical field emission model. As another example, the processing device can acquire the magnetic field intensity emitted by the main magnet to different locations and the maximum electromagnetic field intensity (i.e., the maximum gradient field intensity) that the gradient system can emit to different locations during MRI scanner operation. For each location in the scanning chamber, the processing device can use the sum of the magnetic field strength and the maximum electromagnetic field strength at that location as the physical field strength at that location, thereby determining the physical field emission model.

[0064] The physical field simulation model can be a model used to determine the distribution of physical fields. This model may include convolutional neural networks (CNNs), residual networks (ResNets), etc. In some embodiments, the processing device 110 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 110 can determine the physical field distribution based on the physical field intensity corresponding to each region.

[0065] In some embodiments, the processing device 110 can obtain the physical field distribution information of the nuclear magnetic resonance chamber through the above steps, store the physical field distribution information of the nuclear magnetic resonance chamber in a storage device, and receive the physical field distribution information from the storage device when the nuclear magnetic resonance chamber needs to design a circuit scheme.

[0066] Step 320: Construct a graph structure model based on the physical field distribution information. In some embodiments, step 320 may be performed by the processing device 110 or the model building module 220.

[0067] A graph structure model is a special data structure that includes target nodes and edges, reflecting information about different regions within an MRI chamber and the relationships between them. Each target node in the graph structure model corresponds to a specific location or region within the MRI chamber. Edges in the graph structure model connect two target nodes, reflecting the relationship between the specific locations or regions corresponding to the two target nodes. Edges can have weights, reflecting the properties of the relationships they represent. As an example, an edge can represent a path between two target nodes, and the weight of each edge can reflect how suitable the path between the two target nodes is for wiring. For example, a smaller edge weight indicates that the path between the target nodes is more suitable for wiring. In some embodiments, edges can be directed or undirected.

[0068] In some embodiments, the processing device can construct a house grid of an MRI chamber, the house grid including at least two candidate nodes, and determine at least two target nodes from the at least two candidate nodes. The processing device can also construct a graph structure model by determining at least one edge connecting the at least two target nodes and the weight of each edge based on physical field distribution information. For details on how to construct a graph structure model based on physical field distribution information, please refer to the specification. Figure 4 Some of the explanations will not be described in detail here.

[0069] Step 330: Determine the wiring scheme of the MRI chamber based on the graph structure model. In some embodiments, step 330 may be performed by the processing device 110 or the determination module 230.

[0070] Determining a cabling scheme may include determining one or more of the following: the cabling start point, the cabling end point, the cabling path between the start and end points for each electronic device, the cable material used, and whether anti-magnetic measures are required on the cables. The cabling start point is the beginning of the line in the proposed cabling scheme, and the cabling end point is the end point of the line in the proposed cabling scheme. The cabling start and end points can be set by the cabler or determined based on the spatial layout of the MRI room. Cables can connect the start and end points along the cabling path. In some embodiments, the cabling start point can be a power port, network port, etc., of the MRI room, and the cabling end point can be a port (such as a socket) that provides power or network access to the MRI scanner. The determined cabling path allows cables to connect the power port of the MRI room to the power supply terminals of various instruments (e.g., the MRI scanner) within the MRI room, supplying power, network access, etc., to the instruments within the MRI room.

[0071] In some embodiments, since cables may be affected by physical fields, the distribution of physical fields needs to be considered when determining the wiring scheme, and paths with minimal physical field influence should be selected. For example, the total physical field strength of the locations traversed by the wiring path can be less than a certain threshold. In some embodiments, the building structure of the MRI room also needs to be considered when determining the wiring scheme, avoiding locations where wiring is not allowed, such as locations with partitions. In some embodiments, the difficulty of future maintenance also needs to be considered when determining the wiring scheme. For example, wiring can be avoided in locations where items (such as MRI machines) may be placed in the future. In some embodiments, the cost of wiring also needs to be considered when determining the wiring scheme. For example, under the condition of minimal physical field influence, the shortest wiring path can be selected to save on wiring costs. Therefore, in some embodiments, the processing device can determine a suitable wiring path based on a graph structure model, comprehensively considering information such as the physical field distribution and spatial structure of the MRI room.

[0072] In some embodiments, the processing device can determine a first node corresponding to the starting point of the wiring and a second node corresponding to the ending point of the wiring in the graph structure model. The processing device can further determine at least one wiring path connecting the first node and the second node based on the weight of each edge in the graph structure model, wherein the weights of the edges in each wiring path satisfy a preset condition. In some embodiments, the processing device can utilize a shortest path algorithm to determine the wiring path based on the graph structure model. Exemplary shortest path algorithms may include Bellman-Ford's Algorithm and Dijkstra's Algorithm. For details on how to determine the wiring scheme of the MRI room based on the graph structure model, please refer to the specification. Figure 5 Some of the explanations will not be described in detail here.

[0073] In some embodiments, the processing device can present a determined wiring scheme to a user through a virtual reality device (e.g., virtual reality device 130). Specifically, the processing device can generate a virtual reality model based on the physical field distribution information of the MRI room and the wiring scheme, and then present the virtual reality model using a virtual reality device. A virtual reality model is a model simulated in a virtual digital space using virtual reality technology. The virtual reality model can be displayed through a virtual reality device, allowing the user wearing the virtual reality device to intuitively perceive the simulated virtual reality model through senses such as sight and hearing.

[0074] As an example only, the processing device can generate a 3D model of the MRI chamber based on 3D scan data. For instance, the processing device can use 3D modeling technology to process the 3D scan data to construct a 3D model representing the MRI chamber. If the MRI scanner and other equipment are not placed in the MRI chamber, the 3D model can be generated based on simulated 3D scan data. Furthermore, the processing device can employ virtual reality technology to extend the 3D model of the MRI chamber, assigning material and texture features to the internal space of the MRI chamber and the equipment located within it, generating a 3D virtual view of the MRI chamber. The processing device can then perform 3D rendering on the 3D virtual view of the MRI chamber to present a vivid and realistic 3D virtual effect. Further, the processing device can process the 3D virtual effect based on physical field distribution information and wiring schemes to generate a virtual reality model. For example, the processing device can display the physical field distribution information of the MRI chamber, such as a magnetic field line distribution map, in the virtual reality model using lines of a specific color. For example, the processing device can display defined wiring paths in a virtual reality model by highlighting them in bold, thus distinguishing them from the surrounding environment. Simultaneously, the processing device can use different colored lines to represent the use of different types of cables. In some embodiments, the processing device can process the 3D virtual rendering based solely on the wiring scheme.

[0075] In some embodiments, the processing device can transmit the generated virtual reality model to a virtual reality device, allowing the wearer of the virtual reality device (e.g., a wiring person) to intuitively view the three-dimensional spatial structure, physical field distribution information, and proposed wiring scheme of the MRI room. For example, the wearer can view the wiring path from the starting point to the ending point, the physical field intensity of the areas traversed by the wiring path, and the arrangement of each segment of the wiring path (e.g., above ground or underground). In some embodiments, the virtual reality device may include control components, allowing the wearer to manipulate the virtual reality model. For example, the wearer can use the control components to change the viewing angle of the virtual reality model while in or moving along the wiring path, thereby understanding the wiring path and its surrounding spatial or physical field distribution from different angles.

[0076] In some embodiments, if at least two wiring paths meet preset conditions, the processing device can generate a virtual reality model based on the at least two wiring paths. The virtual reality model can simultaneously display the at least two wiring paths and is then transmitted to a virtual reality device for presentation. Based on this virtual reality model, the wearer can intuitively compare the at least two wiring paths. Furthermore, the wearer can select one of the at least two wiring paths as the final determined wiring path, i.e., the target wiring path. As an example only, the wearer (e.g., a wiring operator) can intuitively understand the wiring direction, wiring method, wiring length, etc., of different wiring paths in the MRI room using the virtual reality device, and select the more suitable wiring path based on their own wiring experience.

[0077] It should be noted that the above description of process 300 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 300 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0078] Figure 4 This is an exemplary flowchart of a method for constructing a graph structure model according to some embodiments of this specification. Process 400 can be executed by a processing device (e.g., processing device 110). For example, process 400 can be implemented as an instruction set (e.g., an application program) stored in memory internal or external to the wiring system 100. The processing device can execute the instruction set and, when executing the instructions, can be configured to execute process 400. The operational schematic diagrams of process 400 presented below are illustrative. In some embodiments, the process can be accomplished using one or more additional operations not described and / or by omitting one or more operations discussed below. Additionally, Figure 4 The order of operations of process 400 shown and described below is not intended to be limiting. In some embodiments, process 400 may be used to implement step 320 of process 300.

[0079] Step 410: Construct a house grid for the MRI chamber, the house grid including at least two alternative nodes. In some embodiments, step 410 may be performed by processing device 110 or model building module 220.

[0080] A house grid is a grid formed by dividing an MRI room or a portion thereof according to its spatial structure. The intersection of each grid cell is a node of the house grid, i.e., a candidate node. In some embodiments, cable laying may be carried out only on or beneath the floor of the MRI room, in which case the floor of the MRI room can be divided to generate the house grid. In some embodiments, cable laying may be carried out on the floor or walls of the MRI room, in which case the floor or walls of the MRI room can be divided to generate the house grid. In some embodiments, the house grid can be a two-dimensional grid or a three-dimensional grid. In some embodiments, the processing device can construct the house grid based on a three-dimensional spatial model of the MRI room or three-dimensional scan data (such as point cloud data), and determine the intersection of each grid cell in the house grid as a candidate node. Each candidate node has a corresponding three-dimensional spatial location within the MRI room. The three-dimensional spatial model of the MRI room can be used to represent the spatial structure inside the MRI room, the floor, the walls, etc., as well as one or more objects that have been or will be placed inside the MRI room.

[0081] The grid cells in a house grid can have the same or different sizes. The grid cells can have any regular or irregular shape, such as rectangles, squares, rhombuses, parallelograms, etc. In some embodiments, the grid cells in a house grid can have the same size. The processing device can construct the house grid based on a 3D spatial model and a set grid size. The number of candidate nodes in the house grid is related to the grid size when dividing the grid; the larger the grid, the fewer the candidate nodes; the smaller the grid, the more candidate nodes. If the grid is too large, the space within the grid may have different structures, making the information at the candidate nodes inaccurate; if the grid is too small, the number of candidate nodes is too large, resulting in a large overall computational load. Therefore, it is necessary to choose an appropriate grid size to obtain an appropriate number of candidate nodes.

[0082] Step 420: Determine at least two target nodes from at least two candidate nodes. In some embodiments, step 420 may be performed by processing device 110 or model building module 220.

[0083] The target node is a node selected from the candidate nodes where wiring can be performed. Considering that some spaces within the MRI room may be unsuitable for wiring, the processing device can filter the candidate nodes to determine the target node. In some embodiments, the processing device may also directly use all candidate nodes as target nodes.

[0084] In some embodiments, the processing device may determine exclusion zones and identify candidate nodes outside the exclusion zones as target nodes. Exclusion zones are portions of the MRI room where wiring is impossible or difficult. In some embodiments, the processing device may determine exclusion zones based on the internal spatial structure of the MRI room. As an example only, exclusion zones may include areas above or below the floor where building components such as slabs, beams, columns, and walls exist. For instance, if an obstacle (e.g., a partition) exists below a certain area of ​​the MRI room floor, preventing wiring from passing through, that area will be considered an exclusion zone, and all candidate nodes within it will not be identified as target nodes. In some embodiments, exclusion zones may also include areas within the MRI room where some difficult-to-move equipment is placed or will be placed, such as the area where the MRI scanner is located.

[0085] In some embodiments, the processing device may determine at least two target nodes from at least two candidate nodes based on the physical field distribution information of the MRI chamber. For example, for each candidate node, the processing device may determine whether the physical field intensity at the corresponding location of each candidate node exceeds a certain physical field intensity threshold. If the physical field intensity of a candidate node is less than the physical field intensity threshold, the candidate node may be determined as a target node. If the physical field intensity of a candidate node is greater than or equal to the physical field intensity threshold, the candidate node will not be determined as a target node. In some embodiments, the physical field intensity threshold may be determined based on the physical field distribution information within the MRI chamber, or it may be determined based on the upper limit of the physical field influence that the cable can be subjected to.

[0086] By identifying exclusion zones and pre-screening candidate nodes based on physical field distribution information, the number of target nodes can be reduced to some extent, eliminating locations where wiring is not possible, thereby reducing the overall computational load of the wiring method.

[0087] Step 430: Based on the physical field distribution information, determine at least one edge connecting at least two target nodes. In some embodiments, step 430 may be performed by the processing device 110 or the model building module 220.

[0088] An edge is a path connecting two target nodes. Each edge can correspond to an actual wiring path within the MRI room, used to assist in determining the wiring scheme. In some embodiments, an edge can connect any two of the at least two target nodes. Alternatively, an edge can connect two adjacent target nodes. In some embodiments, the processing device can determine at least one edge connecting the at least two target nodes and determine the weight of each edge. The weight of an edge can reflect its suitability as an actual wiring path. For example, a smaller edge weight indicates that the edge is more suitable as an actual wiring path. In some embodiments, the weight of an edge is related to one or more factors such as the physical field strength of the two target nodes connected by the edge, the maintenance difficulty of the actual wiring path corresponding to the edge, and the cable material used on the edge.

[0089] For example, for each edge, the processing device can determine the physical field strength of the target node corresponding to the edge based on physical field distribution information, and determine the weight of the edge based on the physical field strength of the target node. Physical field distribution information can include physical field strength information and distribution patterns at different locations within the MRI chamber. Therefore, the processing device can determine the physical field strength at the location of the target node connected to the edge within the MRI chamber based on the physical field distribution information; this is the physical field strength of the target node. The weight of an edge is positively correlated with the physical field strength of the target node it connects to. As an example, the processing device can determine the average physical field strength or the sum of the physical field strengths of the two target nodes connected by the edge as the weight of the edge. The larger the average physical field strength or the sum of the physical field strengths of the two target nodes, the larger the weight of the edge, indicating that the edge is less suitable as a practical wiring path. This reduces the probability of wiring in locations with high physical fields, avoids the influence of physical fields on cables, and thus improves signal transmission efficiency.

[0090] For example, for each edge, the processing device can further determine the probability that an item is placed at the location of the target node corresponding to the edge, and determine the weight of the edge based on the physical field strength and probability. When an item is placed at the location of the target node, if the line at that location fails later, it may be difficult to repair or the repair cost may be high. Therefore, the probability of an item being placed at that location can be further considered to determine the weight of the edge. As an example, if the probability of an item being placed at the location of the target node corresponding to the edge exceeds a certain probability threshold, the processing device can increase the weight of the edge based on the physical field strength of the edge. This can reduce the probability of wiring at the location where an item is placed, avoid moving items during subsequent cable repairs, and thus improve the convenience of repairs.

[0091] For example, for each edge, the processing device can further consider the cost of routing along the path corresponding to the edge and adjust the weight of the edge accordingly. Since cables made of different materials have different material costs and can withstand varying physical field strengths, the processing device can also comprehensively consider both the material cost and the physical field strength to determine the edge's weight. As an example, the processing device can determine the initial weight of the edge and the cable material (e.g., the material with the lowest cost to withstand that physical field strength) based on the physical field strength of the target node corresponding to the edge, and then adjust the initial weight based on the material cost of the selected cable to determine the edge's weight. For instance, different cable materials each correspond to a cost factor; the higher the cable material cost, the higher the cost factor. The processing device can multiply the cost factor corresponding to the selected cable by the initial weight to obtain the adjusted weight. This can reduce the cost of the subsequently determined routing scheme.

[0092] In some embodiments, if anti-magnetic measures are considered during cable routing, the processing device can update the weight of at least one edge based on these measures. Since anti-magnetic measures reduce the influence of physical fields on the cable, the processing device can reduce the weight of the edge. In some embodiments, the anti-magnetic measures may include wrapping the wires to be routed with an anti-magnetic material, which may include copper, foil, aluminum, or other anti-magnetic materials. In some embodiments, the processing device can update the original weight by multiplying the edge's original weight by different percentages based on the different materials of the anti-magnetic material (e.g., copper, aluminum, etc.). In some embodiments, the anti-magnetic measures may be a Ferrari ring or other devices that can reduce the intensity of physical fields.

[0093] Step 440: Construct a graph structure model based on at least two target nodes and at least one edge. In some embodiments, step 440 may be performed by processing device 110 or model building module 220.

[0094] As mentioned above, the graph structure model consists of at least two target nodes and at least one edge connecting the two target nodes. Each target node in the graph structure model can correspond to a specific location within the MRI chamber, and each edge can correspond to a wiring path between the locations of the target nodes it connects to. Edges have weights that reflect the suitability of their corresponding wiring paths as actual wiring paths. The graph structure model, constructed based on the physical field distribution information of the MRI chamber, can reflect the physical field strength of each target node and the suitability of the wiring paths between nodes. Therefore, the graph structure model can be used to determine wiring schemes, effectively improving the efficiency and intelligence of wiring scheme specification. Furthermore, since the edge weights in the graph structure model can be calculated by comprehensively considering the physical field distribution information of the MRI chamber, the probability of object placement, and wiring costs, the wiring schemes formulated based on this graph structure model have higher accuracy and application value.

[0095] In some embodiments, the number of target nodes and the number of edges can be any natural number greater than 2. Considering that calculating the weight of the edge is meaningless when the graph structure model includes only two target nodes and one corresponding edge, the graph structure model can include multiple target nodes and multiple corresponding edges. For example, the number of target nodes can be 5, 10, 20, 40, 50, etc. In some embodiments, the number of target nodes is related to the size of the house grid and the selection criteria for the candidate nodes.

[0096] It should be noted that the above description of process 400 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 400 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0097] Figure 5 This is an exemplary flowchart illustrating the determination of a routing scheme according to some embodiments of this specification. Flow 500 can be executed by a processing device (e.g., processing device 110). For example, flow 500 can be implemented as a set of instructions (e.g., an application program) stored in memory internal or external to the routing system 100. The processing device can execute the instruction set and, when executing the instructions, can be configured to execute flow 500. The operational schematics of flow 500 presented below are illustrative. In some embodiments, the process can be accomplished using one or more additional operations not described and / or by omitting one or more operations discussed below. Additionally, Figure 5 The order of operations of process 500 shown and described below is not intended to be limiting. In some embodiments, process 500 may be used to implement step 330 of process 300.

[0098] Step 510: Determine the first node corresponding to the start point of the wiring and the second node corresponding to the end point of the wiring in the graph structure model. In some embodiments, step 510 may be performed by the processing device 110 or the determining module 230.

[0099] The cabling start point is the beginning of the proposed cabling scheme, and the cabling end point is the end point of the proposed cabling scheme. Both the start and end points are located within the MRI room and can be set by the cabler or determined based on the spatial layout of the MRI room. Cables can connect the start and end points along the cabling path. In some embodiments, the start point can be a power port, network port, etc., within the MRI room, and the end point can be a port (such as a socket) providing power or network access to the MRI scanner. The determined cabling path allows cables to connect the power port of the MRI room to the power supply terminals of various instruments (e.g., the MRI scanner) within the MRI room, supplying power, network access, etc., to the instruments within the MRI room.

[0100] The first node is the target node corresponding to the starting point of the wiring in the graph model structure, and the second node is the target node corresponding to the ending point of the wiring in the graph model structure. The first node and the second node can be used to determine a wiring path in the graph structure model with the first node as the starting point and the second node as the ending point. Each target node in the graph structure model can correspond to a specific location in the MRI room. The processing device can select the target node whose location is closest to the starting point of the wiring as the first node and the target node whose location is closest to the ending point of the wiring as the second node. Step 520: Based on the weight of each edge in the graph structure model, determine at least one wiring path connecting the first node and the second node, wherein the weights of the edges on each wiring path satisfy a preset condition. In some embodiments, step 520 can be executed by the processing device 110 or the determination module 230.

[0101] A wiring path is the route along which wires are laid out from the starting point to the ending point, and it can be used to assist wiring personnel in laying out lines. In some embodiments, the processing device can use a shortest path algorithm based on a graph structure model to determine the wiring path. Exemplary shortest path algorithms may include the Bellman-Ford algorithm and the Dixlat algorithm. In some embodiments, the sum of the weights of the edges on each wiring path must meet preset conditions, such as the sum of weights being less than a weight threshold, or being among the top N (N can be any integer such as 1, 2, or 3) with the smallest sum of weights among all possible wiring paths. As mentioned above, the weights of edges in the graph structure model can be determined based on one or more factors such as the physical field strength of the two target nodes connected to the edge, the maintenance difficulty of the actual wiring path corresponding to the edge, and the cable material used on the edge. Selecting a wiring path based on the sum of weights means comprehensively considering one or more of the above factors, and thus the selected wiring path can meet specific conditions. For example, a wiring path that is less affected by the physical field, has lower maintenance difficulty, and / or lower cost can be selected. In some embodiments, the preset conditions may include the total length of the wiring path being less than a length threshold to avoid excessively long wiring paths that would lead to excessively high wiring costs. In some embodiments, the preset conditions can be set based on the actual conditions of the MRI room.

[0102] For example only, Figure 6 This is a schematic diagram illustrating the determination of wiring paths according to some embodiments of this specification. For example... Figure 6 As shown, V0 is the first node, V8 is the second node, and V1 to V7 are the other target nodes. The lines connecting two nodes represent edges connecting the target nodes, and the data on the lines represent the edge weights. Assuming the preset condition is that the sum of the edge weights on the routing path is minimized, the processing device can determine the routing path from V0 to V8 that satisfies this preset condition based on the Dixlade algorithm. The specific calculation method is described below:

[0103] First, starting from the first node V0, the adjacent nodes of the first node V0 include nodes V1, V2, and V4. The weight from the first node V0 to node V1 is 1, which can be determined by W. 0-1 =1 indicates that the weight W from the first node V0 to the node V2 is... 0-2 The weight W is 5, representing the weights from the first node V0 to the fourth node V4. 0-4 If the value is 6, then node V1 can be selected as the next node to proceed on the path.

[0104] Next, starting from node V1, the nodes adjacent to node V1 include nodes V2, V4, and V3, where the weight W from node V1 to node V2 is... 1-2 The weight W of nodes V1 to V4 is 3. 1-4 The weight W from node V1 to node V3 is 5.1-3 If the weight is 7, then node V2 can be selected as the next node after node V1. The total weight W from the first node V0 through node V1 to node V2 is... 0-1-2 =1+3=4. At this point, we can also verify whether the total weight of traveling from the first node to node V2 via other paths is less than the total weight of that path. For example, the total weight W of traveling directly from the first node V0 to node V2... 0-2 =5, which is greater than the total weight W from the first node V0 through node V1 to node V2. 0-1-2 If W 0-2 Less than W 0-1-2 If so, then node V2 is directly taken as the next node after the first node V0.

[0105] Then, starting from node V2, the nodes adjacent to node V2 include nodes V4 and V5, where the weight W from node V2 to node V4 is... 2-4 The weight W from node V2 to node V5 is 1. 2-5 If the weight is 7, then node V4 can be chosen as the next node after node V2. The total weight W from the first node V0 through nodes V1 and V2 to node V4 is... 0-1-2-4 =1+3+1=5. At this point, the total weight W from the first node V0 through node V1 directly to node V4 is... 0-1-4 =1+5=6, which is greater than W 0-1-2-4 =5, so there's no need to consider this path. Therefore, we can determine that node V4 is the next node after node V2.

[0106] Continuing the above calculations, we can conclude that the next node after node V4 is V3, and W 0-1-2-4-3 =1+3+1+2=7, which is less than W 0-1-3 =1 + 7 = 8, there is no path with a smaller total weight, so there is no need to update the already determined path. The next node of node V3 is node V6, and W 0-1-2-4-3-6 =1+3+1+2+3=10, which is less than W 0-1-2-4-6 =1+3+1+6=11; the next node after node V6 is node V7, and W 0-1-2-4-3-6-7 =1+3+1+2+3+2=12, which is less than W 0-1-2-4-7 =1+3+1+9=14 and W 0-1-2-4-5-7 =1+3+1+3+5=13; the next node after node V7 is V8, and W 0-1-2-4-3-6-7-8 =1+3+1+2+3+2+4=16, less than and W 0-1-2-4-3-6-8 =1+3+1+2+3+7=17.

[0107] In summary, after calculating the possible routes for each node, the path with the minimum total weight from the first node V0 to the second node V8 can be determined as follows: starting from the first node V0, passing through nodes V1, V2, V4, V3, V6, and V7 to the second node V8 (e.g., the path with the minimum total weight). Figure 6 (As shown by the dashed path in the diagram). In some embodiments, the processing device may determine the path connecting the first node and the second node as defined above in the graph structure model as a wiring path.

[0108] In some embodiments, the determined wiring path can be presented to the wearer through a virtual reality device. For details, please refer to the relevant description of step 330, which will not be repeated here.

[0109] In some embodiments, the processing device can determine multiple wiring paths connecting the first node and the second node. For example, in... Figure 6 In the example shown, the total weight of the path from the first node V0 through nodes V1, V2, V4, V3, and V6 to the second node V8 is 17. If the preset condition is that the sum of the weights of the edges on the routing path is less than or equal to the weight threshold of 18, then the above two paths also satisfy the preset condition. At this time, there are at least two determined routing paths (for example, there are three routing paths), and step 530 can be executed.

[0110] Step 530: If at least one wiring path includes at least two wiring paths, a target wiring path is determined from the at least two wiring paths based on the lengths of the at least two wiring paths. In some embodiments, step 530 may be performed by processing device 110 or determining module 230.

[0111] In a graph structure model, the weight of an edge reflects its suitability as an actual wiring path, while the length of the wiring path reflects the wiring cost. Therefore, under certain preset conditions, the actual wiring cost can also be considered when selecting a target wiring path. In some embodiments, the processing device can select the shortest path from at least two wiring paths that meet preset conditions as the target wiring path based on the total length of the wiring paths. The target wiring path is the finally determined, implementable wiring path. In some embodiments, the target wiring path can be presented to the wiring operator through a virtual reality device.

[0112] In such Figure 6In the example shown, the path starting from node V0 and passing through nodes V1, V2, V4, V3, V6, and V7 to node V8 is the first routing path, with a total weight of 16. The path starting from node V0 and passing through nodes V1, V2, V4, V3, and V6 to node V8 is the second routing path, with a total weight of 17. The path starting from node V0 and passing through node V4 to node V8 is the third routing path, with a total weight of 18. In this case, the difference between the total weights of the first and second routing paths is only 1, and the difference between the total weights of the second and third routing paths is also only 1, making them suitable choices for routing paths. However, since the first routing path passes through node V7 more than the second routing path, its total length is greater than that of the second routing path. Similarly, since the second routing path passes through nodes V1, V2, V3, and V6 more than the third routing path, its total length is greater than that of the third routing path. At this point, the processing device can select the third wiring path as the target wiring path from the first wiring path, the second wiring path and the third wiring path, thereby reducing wiring costs.

[0113] In some embodiments, the processing device may also present at least two determined wiring paths to the wearer (e.g., a wiring operator) via a virtual reality device for selection. In some embodiments, if the at least one wiring path includes at least two wiring paths, the processing device may present both wiring paths to the wearer of the virtual reality device via the virtual reality device and receive the target wiring path determined by the wearer from the at least two wiring paths. The wearer (e.g., a wiring operator) can intuitively understand the wiring direction, wiring method, wiring length, etc., of different wiring paths in the MRI room through the virtual reality device and select the more suitable wiring path based on their own wiring experience.

[0114] In some embodiments, there may not be at least one wiring path that meets the preset conditions. For example, if the total weight of all paths considered during step 520 is greater than a weight threshold, it means that there is no suitable wiring path that makes the physical field influence on the wires to be laid within an acceptable range. In this case, the processing device may execute step 540.

[0115] Step 540: If there is no wiring path that meets the preset conditions, update the weight of at least one edge based on the anti-magnetic measures, and determine at least one wiring path connecting the first node and the second node based on the updated weight. In some embodiments, step 540 may be performed by the processing device 110 or the determining module 230.

[0116] In some embodiments, the antimagnetic measures may include wrapping the cables to be laid with antimagnetic material, which may include copper, foil, aluminum, or other antimagnetic materials. In some embodiments, the antimagnetic measures may include installing Ferrari rings or other devices that can reduce the intensity of physical fields.

[0117] In some embodiments, since implementing antimagnetic measures can reduce the physical field strength in the MRI room or increase the cable's resistance to the physical field, thereby reducing the influence of the physical field on the cable, the processing device can update the weight of the at least one edge based on the antimagnetic measures. For example, different antimagnetic materials can correspond to different update coefficients. If the cable corresponding to a certain edge is to be wrapped with antimagnetic material, the processing device can multiply the original weight of the edge by the update coefficient corresponding to the antimagnetic material to update the edge weight. Further, the processing device can determine at least one wiring path connecting the first node and the second node based on the updated weight. The method of determining the wiring path based on the updated weight is similar to the method of determining the wiring path based on the original weight; please refer to the relevant description in step 520, which will not be repeated here.

[0118] In some embodiments, for multiple pairs of wiring start points and wiring end points, the processing device may execute process 500 for each pair of wiring start points and wiring end points to determine the wiring path corresponding to each pair of wiring start points and wiring end points respectively.

[0119] It should be noted that the above description of process 500 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 500 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0120] The beneficial effects that the embodiments of this specification may bring include, but are not limited to: (1) By combining physical field distribution information and graph structure model, the wiring scheme can be quickly determined by artificial intelligence technology, which can effectively improve the efficiency and intelligence of the wiring scheme; (2) Through virtual reality equipment, the wearer can intuitively view the three-dimensional spatial structure, physical field distribution information and proposed wiring scheme of the MRI room; (3) By determining the exclusion area and pre-screening the candidate nodes based on the physical field distribution information, the number of target nodes can be reduced to a certain extent, and the positions that cannot be wired can be excluded, thereby reducing the overall computational load of the wiring method; (4) The weight of the edge in the graph structure model is calculated by comprehensively considering the physical field distribution information of the MRI room, the probability of placing items and the wiring cost, etc. The wiring scheme formulated based on the graph structure model has higher accuracy and application value; (5) Determining multiple wiring paths and determining the target wiring path from them based on the path length can reduce the wiring cost.

[0121] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects may be any one or a combination of the above, or any other possible beneficial effects.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 wiring method, executed by at least one processor, characterized in that, The method includes: Obtain the physical field distribution information of the nuclear magnetic resonance chamber to be wired; Construct a house grid for the nuclear magnetic resonance chamber, the house grid including at least two alternative nodes; Determine at least two target nodes from the at least two candidate nodes; Based on the physical field distribution information, at least one edge connecting the at least two target nodes is determined; and Based on the at least two target nodes and the at least one edge, a graph structure model is constructed, including: for each edge of the at least one edge, Based on the physical field distribution information, the physical field strength of the target node corresponding to the edge is determined; and The weight of the edge is determined based on the physical field strength of the target node corresponding to the edge; and Based on the graph structure model, the wiring scheme of the nuclear magnetic resonance chamber is determined.

2. The method as described in claim 1, characterized in that, The process of determining the wiring scheme for the nuclear magnetic resonance chamber based on the graph structure model includes: Determine the first node corresponding to the start point of the wiring and the second node corresponding to the end point of the wiring in the graph structure model; and Based on the weight of each edge in the graph structure model, at least one wiring path connecting the first node and the second node is determined, and the weight of the edges on each wiring path satisfies a preset condition.

3. The method as described in claim 2, characterized in that, The at least one wiring path includes at least two wiring paths, and determining the wiring scheme of the MRI room based on the graph structure model further includes: Based on the lengths of the at least two wiring paths, a target wiring path is determined from the at least two wiring paths.

4. The method as described in claim 2, characterized in that, The step of determining the wiring scheme of the nuclear magnetic resonance chamber based on the graph structure model further includes: If no wiring path meets the preset conditions, update the weight of the at least one edge based on the antimagnetic protection measures; and Based on the weights updated by the at least one edge, the at least one wiring path connecting the first node and the second node is determined.

5. The method as described in claim 1, characterized in that, The method further includes: Based on the physical field distribution information of the nuclear magnetic resonance chamber and the wiring scheme, a virtual reality model is generated; and The virtual reality model is presented using virtual reality devices.

6. The method as described in claim 1, characterized in that, The process of obtaining the physical field distribution information of the nuclear magnetic resonance chamber to be wired includes: Obtain a three-dimensional spatial model of the nuclear magnetic resonance chamber; Acquire characteristic information of the nuclear magnetic resonance spectrometer, the characteristic information including at least the physical field emission model of the nuclear magnetic resonance spectrometer; and Based on the three-dimensional spatial model and the feature information, the physical field distribution information is determined using a physical field simulation model.

7. The method of claim 1, wherein the physical field comprises at least one of a magnetic field and an electromagnetic field.

8. A cabling system, characterized in that, include: The acquisition module is used to acquire the physical field distribution information of the nuclear magnetic resonance chamber to be wired; The model building module is used for: Construct a house grid for the nuclear magnetic resonance chamber, the house grid including at least two alternative nodes; Determine at least two target nodes from the at least two candidate nodes; Based on the physical field distribution information, determine at least one edge connecting the at least two target nodes; as well as Based on the at least two target nodes and the at least one edge, a graph structure model is constructed, including: for each edge of the at least one edge, Based on the physical field distribution information, the physical field strength of the target node corresponding to the edge is determined; and The weight of the edge is determined based on the physical field strength of the target node corresponding to the edge; and The determination module is used to determine the wiring scheme of the nuclear magnetic resonance chamber based on the graph structure model.

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