Cooperative control method and system of bed-moving magnetic resonance scanning system, medium, program and electronic terminal

Abstract

The invention provides a cooperative control method and system of a bed moving type magnetic resonance scanning system, a medium, a program and an electronic terminal. According to the end time T0 of a current collection sequence, the bed moving time tij corresponding to each remaining to-be-collected sequence Aij and the predicted waiting time uij of each remaining to-be-collected sequence Aij, the bed moving time tij and the predicted waiting time uij of each remaining to-be-collected sequence Aij are calculated; and selecting a next to-be-executed target acquisition sequence Atarget from the remaining to-be-acquired sequences Aij on the basis of a shortest time principle. The moving process of the scanning bed is arranged to be overlapped with'garbage time 'which is not suitable for collection in the physiological cycle of a patient, so that when a bed body accurately arrives at a target position, the physiological state of the patient is just or is about to enter a corresponding target triggering time phase; the two processes of'bed moving 'and'waiting time phase' which originally must be executed in series are parallelized to the maximum extent, and the scanning efficiency and the equipment throughput are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of intelligent control of magnetic resonance scanning equipment, and in particular to a collaborative control method, system, medium, program and electronic terminal for a bed-moving magnetic resonance scanning system. Background Technology

[0002] Existing bed-moving magnetic resonance imaging (MRI) scanners typically employ a fixed and progressive scanning bed movement control method. Specifically, after completing the previous acquisition sequence, the scanning bed is controlled to move along the original direction of motion to the next preset bed position. After the bed movement is completely completed, physiological signals are monitored, waiting for the specific physiological phase required by the acquisition sequence at that bed position to appear, and data acquisition is triggered when that phase arrives.

[0003] However, this fixed procedure of sequentially executing bed movement and waiting for physiological phases has a significant efficiency bottleneck: because the start and stop times of bed movement are completely independent of the patient's autonomous physiological rhythms, the system is highly likely to wait for half or even a complete physiological cycle before triggering data acquisition when the bed movement ends. This problem becomes even more pronounced when using single respiratory gating (the cycle is usually 4-6 seconds) or dual gating of respiration and heart rate (the cycle is usually more than 8 seconds).

[0004] This inefficient operating model directly leads to longer MRI scan times for each patient, which not only reduces equipment throughput and exacerbates patient queuing, but also wastes valuable medical resources and further intensifies the contradiction between medical supply and demand. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a collaborative control method, system, medium, program and electronic terminal for a bed-moving magnetic resonance scanning system to solve the aforementioned problems.

[0006] To achieve the above and other related objectives, the first aspect of this application provides a collaborative control method for a bed-moving magnetic resonance imaging (MRI) scanning system, comprising: based on the test end time T0 of the current acquisition sequence and the remaining acquisition sequences A... ij The corresponding bed moving time △t ij And waiting time △u ij Based on the principle of shortest time, from the remaining sequence A to be collected ij Select the next target acquisition sequence A to be executed. target Wherein, the bed moving time Δt ij To move the scanning bed from the position corresponding to the current acquisition sequence to the position of the sequence to be acquired, A. ij The time required for the corresponding location, the waiting time Δu ijThe duration is the time required from the end of the bed movement for the current acquisition sequence to the arrival time of the corresponding next target trigger phase; 1≤i≤n, 1≤j≤m, where i is the index of the scanning bed position and j is the index of the acquisition sequence under the bed position; at the end of the current acquisition sequence test, the scanning bed is moved from its current position to the target acquisition sequence A. target The corresponding position.

[0007] In one embodiment of the first aspect of this application, the principle of shortest time is based on the remaining sequence A to be collected. ij Select the next target acquisition sequence A to be executed. target The method includes: calculating the remaining sequence A to be collected. ij The corresponding delay time H ij The sequence to be acquired with the smallest delay time is taken as the target acquisition sequence; where H ij =△t ij +△u ij .

[0008] In one embodiment of the first aspect of this application, the principle of shortest time is based on the remaining sequence A to be collected. ij Select the next target acquisition sequence A to be executed. target The methods include: based on the remaining acquisition sequence A to be tested ij Given various permutations and combinations, calculate all A's in each of the given permutations and combinations. ij The corresponding delay time H ij The sum of H total The smallest value of H total The sequence to be acquired, which is the first in the temporal sequence of the corresponding permutation and combination scheme, is taken as the target acquisition sequence; where H ij =△t ij +△u ij .

[0009] In one embodiment of the first aspect of this application, the waiting time Δu ij The determination method includes: fitting second physiological data for a future period of time based on the measured first physiological data of the patient; wherein the types of the first and second physiological data include respiratory data and heart rate data; and determining each sequence A to be collected based on the second physiological data. ij W, the time of each gating triggering phase in the future period ij_s Where s is a sequence A to be collected. ij The index identifier of the corresponding gating trigger phase; select the W value with the smallest value that meets the following requirements. ij_s The moment W is the target trigger phase. ij_target : but,

[0010] In one embodiment of the first aspect of this application, the method for fitting second physiological data for a future period of time based on the measured first physiological data of the patient includes: fitting the first physiological data with a model to obtain the second physiological data; wherein the type of the model includes: autoregressive model, Kalman filter model and machine learning time series prediction model.

[0011] In one embodiment of the first aspect of this application, the scanning bed is moved from its current position to the target acquisition sequence A. target After the corresponding position, when the target acquisition sequence A target When the next target trigger phase arrives, magnetic resonance data is acquired.

[0012] To achieve the above and other related objectives, a second aspect of this application provides a collaborative control system for a bed-moving magnetic resonance imaging (MRI) scanning system, comprising: a target acquisition sequence determination module, used to determine the target acquisition sequence based on the test end time T0 of the current acquisition sequence and each remaining acquisition sequence A. ij The corresponding bed moving time △t ij And waiting time △u ij From the remaining sequence A to be collected ij Select the next target acquisition sequence A to be executed. target Wherein, the bed moving time Δt ij To move the scanning bed from the position corresponding to the current acquisition sequence to the position of the sequence to be acquired, A. ij The time required for the corresponding location, the waiting time Δu ij The duration is the time required from the end of the bed movement for the current acquisition sequence to the arrival time of the corresponding next target trigger phase; 1≤i≤n, 1≤j≤m, where i is the index of the scanning bed position and j is the index of the acquisition sequence under the bed position; the scanning bed drive module is used to move the scanning bed from its current position to the target acquisition sequence A when the current acquisition sequence test ends. target The corresponding position.

[0013] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the preceding claims.

[0014] To achieve the above and other related objectives, a fourth aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to perform the method described in any of the preceding claims.

[0015] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in any of the preceding claims.

[0016] As described above, the collaborative control method for a bed-moving magnetic resonance scanning system of this application has the following beneficial effects:

[0017] This invention fundamentally restructures the workflow of magnetic resonance imaging (MRI) scanning by introducing a collaborative control mechanism based on real-time prediction and dynamic scheduling. Specifically, at each decision point, the system bases its decisions on the end time T0 of the current acquisition sequence and the remaining sequences A to be acquired. ij Corresponding bed moving time △t ij and its estimated waiting time △u ij And based on the principle of shortest time, from the remaining sequence A to be collected. ij Select the next target acquisition sequence A to be executed. target This decision-making mechanism transforms the system from a traditional passive serial mode to an active intelligent scheduling mode. Instead of passively monitoring physiological signals only after the bed is in place, the system continuously predicts future physiological cycle waveforms based on the collected physiological data, thus pre-determining when the target physiological phase required for each acquisition sequence will occur. Through scheduling, the system can arrange the movement of the scanning bed to overlap with the "garbage time" in the patient's physiological cycle that is unsuitable for acquisition. This ensures that when the bed precisely reaches the target position, the patient's physiological state is exactly or about to enter the corresponding target trigger phase (also known as the earliest gated trigger phase of the sequence). This maximizes the parallelization of the originally sequential "bed movement" and "waiting phase" processes, reducing non-measurement time during testing and significantly improving scanning efficiency and equipment throughput. Attached Figure Description

[0018] Figure 1 The diagram shown is a flowchart illustrating a collaborative control method for a bed-moving magnetic resonance scanning system according to an embodiment of this application.

[0019] Figure 2 This is a schematic diagram showing the positions of each gate trigger phase and target trigger phase of each acquisition sequence in the next three physiological cycles in one embodiment of this application.

[0020] Figure 3 The diagram shown is a structural schematic of the collaborative control system of a bed-moving magnetic resonance scanning system according to an embodiment of this application.

[0021] Figure 4 The diagram shown is a structural schematic of an electronic terminal according to an embodiment of this application. Detailed Implementation

[0022] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0023] In the embodiments of this application, terms such as "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, "first XX" and "second XX" are merely used to distinguish different XXs and do not limit their order. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0024] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0025] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0026] like Figure 1 As shown, the first aspect of this application provides a collaborative control method for a bed-moving magnetic resonance scanning system, comprising:

[0027] S1: Based on the test end time T0 of the current acquisition sequence and each remaining acquisition sequence A ij The corresponding bed moving time △t ij And waiting time △uij From the remaining sequence A to be collected ij Select the next target acquisition sequence A to be executed. target Wherein, the bed moving time Δt ij To move the scanning bed from the position corresponding to the current acquisition sequence to the position of the sequence to be acquired, A. ij The time required for the corresponding location, the waiting time Δu ij The duration is the time required from the end of the bed movement of the sequence to be acquired until the arrival of the next target trigger phase; 1≤i≤n, 1≤j≤m, where i is the index of the scanning bed and j is the index of the sequence to be acquired at the bed position.

[0028] It should be understood that in actual magnetic resonance imaging (MRI) scan testing, there are several inherent system-level delays (unavoidable time overheads due to inherent hardware and software workflows). Specifically, for example, after the previous acquisition sequence is completed, approximately 1-3 seconds are typically required for data storage and scan sequence unloading before the scanning bed can be moved. Correspondingly, after the scanning bed reaches the target position, approximately 1-2 seconds are needed for magnetic field stabilization and inter-system handshake communication before the system enters the waiting state for the target physiological phase. Since the aforementioned data storage, sequence unloading, magnetic field stabilization, and handshake communication times are inherent and relatively fixed processing delays, their magnitude is not affected by the scheduling strategy of this invention (the key point of this invention is to reduce bed movement time and post-movement waiting time). Therefore, for simplicity, this invention simplifies the testing process as follows: after the previous acquisition sequence is completed, bed movement can be performed directly (without data storage or sequence unloading); after bed movement, the phase waiting stage can be entered directly (without magnetic field stabilization or handshake communication). This allows the discussion in this paper to focus on the core optimization mechanism of this invention.

[0029] In one embodiment of the first aspect of this application, the waiting time Δu ij The determination method includes: fitting second physiological data for a future period of time based on the measured first physiological data of the patient; wherein the types of the first and second physiological data include respiratory data and heart rate data; and determining each sequence A to be collected based on the second physiological data. ij W, the time of each gating triggering phase in the future period ij_s Where s is a sequence A to be collected. ij The index identifier of the corresponding gating trigger phase; select the W value with the smallest value that meets the following requirements. ij_s The moment W is the target trigger phase. ij_target : but,

[0030] It should be understood that the first physiological data is the physiological data of the patient that has been measured, and the second physiological data is the physiological data of the patient in the future predicted based on the first physiological data.

[0031] Traditional MRI scanners typically move the scanning bed in approximately 3 seconds (using a step-by-step method). For example, with five acquisition sequences corresponding to beds 1-5, where beds with similar sequence numbers are closer together, the traditional method would start testing from the sequence corresponding to bed 1, followed by beds 2, 3, and so on until bed 5. In this case, each step of the scanning bed movement takes about 3 seconds. However, the bed-moving scheme in this invention is not a fixed step-by-step method. Instead, the position of the scanning bed for the next acquisition sequence is randomly determined based on a delay time. Therefore, in the example of the five acquisition sequences corresponding to beds 1-5, a possible bed movement pattern is: starting with the sequence corresponding to bed 2, then beds 1, 5, 3, and finally bed 4. Because the distance between adjacent beds may increase compared to the traditional method, the corresponding scanning time will be extended by approximately 0-3 seconds from the 3-second limit. That is, the bed-moving time in this invention is approximately 3-6 seconds.

[0032] Based on the bed transfer time, the optimization method of this invention is mainly applicable to magnetic resonance imaging (MRI) scanners that employ single respiratory gating or dual gating of respiratory and heart rate. Since the cardiac cycle of single heart rate gating is relatively short (typically about 1 second), its inherent waiting time is already quite limited, and the actual benefit of scheduling optimization using the method of this invention is not significant. Therefore, this invention does not involve MRI scanners that use only single heart rate gating as the physiological gating signal. Therefore, the physiological data in the first and second physiological data includes: single respiratory data, and a combination of respiratory and heart rate data.

[0033] After obtaining the second physiological data, the arrival time W of each gating trigger phase in the future period of each acquisition sequence is determined based on the second physiological data. ij_s It should be understood that the gating triggering phase W occurs at... ij_s This refers to the triggering phase of each acquired sequence within each physiological cycle over a future period of time. For example, such as... Figure 2 As shown, assume there are 3 acquisition sequences (corresponding to 3 beds respectively), namely A 11 A 21 A 31 The triggering phases for each acquisition sequence in the next three periods are as follows: A 11 :W 11_1 W 11_2 W 11_3 A 21 :W 21_1W 21_2 W 21_3 A 31 :W 31_1 W 31_2 W 31_3 .

[0034] Choose the value of W that satisfies the following requirements. ij_s The moment W is the target trigger phase. ij_target : This means selecting the gated trigger phase that is closest to the end of the bed-moving process in the timing sequence as the target trigger phase corresponding to the acquisition sequence. For example, such as... Figure 2 As shown, select W. 11_2 As A 11 For the corresponding target trigger phase, select W. 21_1 As A 21 For the corresponding target trigger phase, select W. 31_3 As A 31 The corresponding target triggering phase.

[0035] Then, based on the target triggering phase of each remaining acquisition sequence, the corresponding waiting time is calculated.

[0036] In one embodiment of the first aspect of this application, the method for fitting second physiological data for a future period of time based on the measured first physiological data of the patient includes: fitting the first physiological data with a model to obtain the second physiological data; wherein the type of the model includes: autoregressive model, Kalman filter model and machine learning time series prediction model.

[0037] Since respiration and heartbeat are both periodic signals with strong autocorrelation, meaning that the state at the next moment is highly dependent on the state at several previous moments, autoregressive models can accurately quantify this dependency, find its inherent cycle and pattern, and thus reliably predict the signal trend for the next few seconds or even tens of seconds based on the most recent historical data. This is especially suitable for data with strong regularity, such as respiratory signals or signals combining respiration and heartbeat, and can obtain more accurate secondary physiological data.

[0038] The Kalman filter model focuses on solving the problem of signal noise interference. In actual scanning, the physiological signals acquired will inevitably be mixed with various noises. The Kalman filter model can dynamically filter out noise and correct the prediction trajectory in real time. Even when faced with imperfect data, it can still output a smooth and reliable prediction curve, thereby greatly enhancing the robustness and accuracy of the predicted second physiological data.

[0039] Machine learning time series prediction models offer more powerful nonlinear fitting and long-term dependency learning capabilities. They can learn more complex physiological patterns from massive amounts of data, including individual-specific breathing habits and nonlinear features such as heart rate variability under different conditions. A fully trained machine learning model, like an experienced doctor, can sensitively detect subtle changes in a patient's physiological state after measuring several cycles of respiratory data or combined respiratory and heart rate data, and make more individualized and forward-looking predictions accordingly.

[0040] In this invention, firstly, based on the test end time T0 of the current acquisition sequence and each remaining acquisition sequence A, ij The corresponding bed moving time △t ij And waiting time △u ij Based on the principle of shortest time, from the remaining sequence A to be collected ij Select the next target acquisition sequence A to be executed. target .in:

[0041] Preferably, in one embodiment of the first aspect of this application, the principle of shortest time is based on the remaining sequence A to be collected. ij Select the next target acquisition sequence A to be executed. target The method includes: calculating the remaining sequence A to be collected. ij The corresponding delay time H ij The sequence to be acquired with the smallest delay time is taken as the target acquisition sequence; where H ij =△t ij +△u ij .

[0042] In this embodiment, the principle of shortest time refers to minimizing the time between the completion of the previous acquisition sequence test and the start of the next acquisition sequence test. That is, this scheme considers only the next acquisition sequence to be executed and selects the one that allows data acquisition and measurement to begin fastest as the target acquisition sequence. For example, assuming the test end time T0 of the current acquisition sequence is 0 seconds, and the remaining acquisition sequences A... ij They are respectively: A 11 A 12 A 13 A 21 A 22 A 31 A 41 A 51 A 61 A 71 A 72 A 73 A 81A total of 13 data acquisition sequences were collected (beds 1 and 7 had three sequences, bed 2 had two sequences, and beds 3-6 and 8 each had one sequence). It was assumed that the maximum bed transfer time would not exceed 6 seconds, and the maximum waiting time for each acquisition sequence would not exceed 6 seconds (when using single respiratory gating, since the respiratory cycle is usually 4-6 seconds, the waiting time will not exceed one physiological cycle; therefore, the maximum waiting time is set to 6 seconds in this example. However, it should be understood that when using dual gating of respiratory and heart rate, the duration of this physiological cycle (i.e., the maximum waiting time) usually exceeds 8 seconds). The bed transfer time Δt corresponding to each acquisition sequence was also calculated. i Waiting time △u ij and delay time H ij As shown in Table 1:

[0043]

[0044] As shown in Table 1, the delay time H in the remaining acquisition sequence ij The shortest acquisition sequence is A. 41 =3.46 seconds. Therefore, the acquisition sequence A will be... 41 As the target acquisition sequence.

[0045] In the traditional approach, the device will follow the acquisition sequence A 11 -A 81 The tests are performed sequentially (especially moving the scanning beds in the order of beds 1-8). When the current acquisition sequence completes the test, the remaining A sequences will be moved directly to the next sequence. 11 -A 81 Select A from these acquisition sequences 11 As the next test sequence, the scan bed is moved to the corresponding position, which requires a delay of 6.24 seconds. However, this embodiment directly uses the acquisition sequence A... 41 As the target acquisition sequence, its latency is only 3.46 seconds, saving nearly 2.78 seconds with this single decision. In a whole-body scan containing more than a dozen sequences, this saving will accumulate repeatedly, resulting in a significant reduction in the total testing time. Through a simple, intelligent, and adaptive scheduling decision, it intelligently synchronizes the movement of the scanning bed with the patient's physiological cycle, effectively utilizing previously wasted time. This optimizes the scanning process at the underlying logic level, significantly improving the hospital's equipment throughput per unit time and providing key technical support for improving the operational efficiency of the entire medical imaging department.

[0046] Preferably, in one embodiment of the first aspect of this application, the principle of shortest time is based on the remaining sequence A to be collected. ij Select the next target acquisition sequence A to be executed. target The methods include: based on the remaining acquisition sequence A to be testedij Given various permutations and combinations, calculate all A's in each of the given permutations and combinations. ij The corresponding delay time H ij The sum of H total The smallest value of H total The sequence to be acquired, which is the first in the temporal sequence of the corresponding permutation and combination scheme, is taken as the target acquisition sequence; where H ij =△t ij +△u ij .

[0047] In this embodiment, the principle of shortest time refers to the fact that at each decision-making stage, based on the various permutations and combinations of the remaining sequences to be collected, all A... ij The corresponding delay time H ij The sum of H total Shortest. It is not limited to selecting the fastest single sequence to execute, but takes into account all remaining sequences to be collected, and finds the optimal execution path that can achieve the shortest total scan time by evaluating all possible execution orders.

[0048] Specifically, this method first bases on all remaining sequences A to be collected. ij The system generates various possible permutations and combinations, each representing a complete subsequent scan execution order. Based on this, the system performs a full-process simulation for each permutation and combination, calculating the delay time H corresponding to all sequences under that scheme. ij (i.e., bed moving time △t) ij With waiting time △u ij The cumulative value H of the sum of (the sum of the sums ... total This metric accurately reflects the total time cost required to execute this order, by comparing the H values ​​corresponding to all permutations and combinations. total The system can precisely locate the optimal execution plan with the shortest total time (this plan is theoretically the globally optimal solution that can complete all remaining scanning work the fastest). Finally, the system determines the sequence to be acquired that is first in the time sequence of this optimal plan as the target acquisition sequence A to be executed immediately. target This solution breaks through the limitations of local optima and achieves true global optimization. It can effectively avoid long-term efficiency losses caused by short-term gains, ensure that the scanning process always proceeds along the most efficient path, minimize equipment idle time, and improve overall scanning efficiency.

[0049] S2: At the end of the current acquisition sequence test, move the scan bed from its current position to the target acquisition sequence A. target The corresponding position.

[0050] It should be understood that at the end of the current acquisition sequence test, the scan bed will be moved from its current position to the target acquisition sequence A. target The corresponding position represents the key action in this invention's intelligent scheduling decision-making process, moving from "calculation" to "execution." Through this step, the system successfully and deeply integrates the originally independent "bed movement time" with the "waiting time" within the physiological cycle. As the scanning bed moves towards the target position under mechanical drive, the patient's physiological rhythm evolves synchronously. This ingenious spatiotemporal coordination ensures that the moment the bed arrives at the target position highly coincides with the moment the patient enters the ideal acquisition phase, thereby achieving parallelization of the "bed movement" and "waiting phase" processes at the system level.

[0051] In one embodiment of the first aspect of this application, a collaborative control method for a bed-moving magnetic resonance scanning system further includes:

[0052] S3: Move the scanning bed from its current position to the target acquisition sequence A. target After the corresponding position, when the target acquisition sequence A target When the next target trigger phase arrives, magnetic resonance data is acquired.

[0053] like Figure 3 As shown, a second aspect of this application provides a collaborative control system for a bed-moving magnetic resonance scanning system, including: a target acquisition sequence determination module, used to determine the target acquisition sequence based on the test end time T0 of the current acquisition sequence and each remaining acquisition sequence A. ij The corresponding bed moving time △t ij And waiting time △u ij From the remaining sequence A to be collected ij Select the next target acquisition sequence A to be executed. target Wherein, the bed moving time Δt ij To move the scanning bed from the position corresponding to the current acquisition sequence to the position of the sequence to be acquired, A. ij The time required for the corresponding location, the waiting time Δu ij The duration is the time required from the end of the bed movement for the current acquisition sequence to the arrival time of the corresponding next target trigger phase; 1≤i≤n, 1≤j≤m, where i is the index of the scanning bed position and j is the index of the acquisition sequence under the bed position; the scanning bed drive module is used to move the scanning bed from its current position to the target acquisition sequence A when the current acquisition sequence test ends. target The corresponding position.

[0054] It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0055] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0056] A third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the preceding claims.

[0057] A fourth aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to perform the method described in any of the preceding claims.

[0058] like Figure 4 As shown, a fifth aspect of this application provides an electronic terminal including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described in any of the preceding claims. The electronic terminal includes at least one processor 101, a memory 102, at least one network interface 103, and a user interface 105. The various components in the device are coupled together via a bus system 104. It is understood that the bus system 104 is used to enable communication between these components. In addition to a data bus, the bus system 104 also includes a power bus, a control bus, and a status signal bus.

[0059] The user interface 105 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0060] It is understood that memory 102 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0061] In this embodiment of the invention, the memory 102 is used to store various types of data to support the operation of the electronic terminal 100. Examples of this data include: any executable program for operation on the electronic terminal 100, such as the operating system 1021 and application programs 1022; the operating system 1021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 1022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The methods provided in this embodiment of the invention may be included in the application program 1022.

[0062] The methods disclosed in the above embodiments of the present invention can be applied to processor 101, or implemented by processor 101. Processor 101 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 101 or by instructions in the form of software. The processor 101 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 101 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 101 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in a memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0063] In an exemplary embodiment, the electronic terminal 100 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0064] The terms “component,” “module,” “system,” etc., used in this specification are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0065] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0066] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0067] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0068] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0070] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs, DVDs), or semiconductor media (e.g., solid-state disks, SSDs, etc.).

[0071] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0073] In summary, this application effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0074] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A collaborative control method for a bed-moving magnetic resonance scanning system, characterized in that, include: Based on the test end time T0 of the current acquisition sequence and the remaining acquisition sequences A ij The corresponding bed moving time △t ij And waiting time △u ij Based on the principle of shortest time, from the remaining sequence A to be collected ij Select the next target acquisition sequence A to be executed. target Wherein, the bed moving time Δt ij To move the scanning bed from the position corresponding to the current acquisition sequence to the position of the sequence to be acquired, A. ij The time required for the corresponding location, the waiting time Δu ij The duration required from the end of the bed transfer of the sequence to be collected until the arrival of the corresponding next target trigger phase; 1≤i≤n, 1≤j≤m, where i is the index identifier of the scanning bed position and j is the index identifier of the acquisition sequence under the bed position; At the end of the current acquisition sequence test, the scan bed is moved from its current position to the target acquisition sequence A. target The corresponding position.

2. The collaborative control method for a bed-moving magnetic resonance scanning system according to claim 1, characterized in that, The principle of shortest time is used to select from the remaining sequence A to be collected. ij Select the next target acquisition sequence A to be executed. target The methods include: Calculate each remaining sequence A to be collected ij The corresponding delay time H ij The sequence to be acquired with the smallest delay time is taken as the target acquisition sequence; where H ij =△t ij +△u ij .

3. The collaborative control method for a bed-moving magnetic resonance scanning system according to claim 1, characterized in that, The principle of shortest time is used to select from the remaining sequence A to be collected. ij Select the next target acquisition sequence A to be executed. target The methods include: Based on the remaining acquisition sequence A ij Given various permutations and combinations, calculate all A's in each of the given permutations and combinations. ij The corresponding delay time H ij The sum of H total The smallest value of H total The sequence to be acquired, which is the first in the temporal sequence of the corresponding permutation and combination scheme, is taken as the target acquisition sequence; where H ij =△t ij +△u ij .

4. The collaborative control method for a bed-moving magnetic resonance scanning system according to claim 1, characterized in that, The waiting time △u ij The methods for determining this include: Based on the patient's first physiological data that has been measured, second physiological data for a future period of time is fitted; wherein, the types of the first and second physiological data include respiratory data and heart rate data; Based on the second physiological data, each sequence A to be collected is determined. ij W, the time of each gating triggering phase in the future period ij_s Where s is a sequence A to be collected. ij The index identifier of the corresponding gating trigger phase arrival time; Choose the value of W that satisfies the following requirements. ij_s The moment W is the target trigger phase. ij_target : but, 5. The collaborative control method for a bed-moving magnetic resonance scanning system according to claim 4, characterized in that, The method for fitting second physiological data for a future period based on the measured first physiological data of the patient includes: fitting the first physiological data with a model to obtain the second physiological data; wherein the types of the model include: autoregressive model, Kalman filter model and machine learning time series prediction model.

6. The collaborative control method for a bed-moving magnetic resonance scanning system according to claim 1, characterized in that, Move the scanning bed from its current position to the target acquisition sequence A. target After the corresponding position, when the target acquisition sequence A target When the next target trigger phase arrives, magnetic resonance data is acquired.

7. A collaborative control system for a bed-moving magnetic resonance scanning system, characterized in that, include: The target acquisition sequence determination module is used to determine the target acquisition sequence based on the test end time T0 of the current acquisition sequence and the remaining acquisition sequences A. ij The corresponding bed moving time △t ij And waiting time △u ij From the remaining sequence A to be collected ij Select the next target acquisition sequence A to be executed. target Wherein, the bed moving time Δt ij To move the scanning bed from the position corresponding to the current acquisition sequence to the position of the sequence to be acquired, A. ij The time required for the corresponding location, the waiting time Δu ij The duration is the time required from the end of the bed movement of the sequence to be acquired until the arrival of the next target trigger phase; 1≤i≤n, 1≤j≤m, where i is the index of the scanning bed and j is the index of the sequence to be acquired at the bed position. The scanning bed drive module is used to move the scanning bed from its current position to the target acquisition sequence A when the current acquisition sequence test ends. target The corresponding position.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-6.

9. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to implement the method as described in any one of claims 1-6.

10. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1-6.

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