Magnetic resonance scanning control method and device, computer equipment and storage medium

Abstract

The invention relates to a magnetic resonance scanning control method and device, computer equipment and a storage medium. The method comprises the following steps: aiming at target stimulation applied to a detected object, acquiring a first magnetic resonance data set of the detected object in stimulation response time and a second magnetic resonance data set of the detected object in resting response time; respectively performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set to obtain a first magnetic resonance image of the detected object in the stimulation response time and a second magnetic resonance image of the detected object in the resting response time; determining a detection result of the detection object according to the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the influence degree of the target stimulation on the detection object. By adopting the method, the imaging quality of the magnetic resonance image can be improved, so that the accuracy and feasibility of stimulation monitoring on the detected object are ensured.

Description

Technical Field

[0001] The present application relates to the field of medical imaging technology, and in particular to a magnetic resonance scanning control method, a magnetic resonance scanning control device, a computer device, a storage medium and a computer program product. Background Art

[0002] Magnetic Resonance Imaging (MRI) is a high-tech medical imaging technology that uses an external magnetic field and radio frequency pulses to make the hydrogen nuclei in the test object (such as human tissue) resonate to generate signals, and then the signals are processed by a computer to form a magnetic resonance image. MRI technology has become one of the most advanced tools in medical examinations due to its advantages such as no ionizing radiation and multi-directional imaging.

[0003] However, if the imaging quality of the magnetic resonance images obtained after the object is detected using MRI technology is poor, it is not conducive to the accuracy of medical diagnosis. Summary of the invention

[0004] Based on this, it is necessary to provide a magnetic resonance scanning control method, a magnetic resonance scanning control device, a computer device, a storage medium and a computer program product that can improve the imaging quality of magnetic resonance detection images in response to the above technical problems.

[0005] In a first aspect, the present application provides a magnetic resonance scanning control method. The method comprises:

[0006] In response to a target stimulus applied to a detection object, a first magnetic resonance data set of the detection object within a stimulus response time and a second magnetic resonance data set of the detection object within a rest response time are acquired;

[0007] Respectively performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image of the detection object within the rest response time;

[0008] A detection result of the detection object is determined according to a difference between target pixels in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the degree of influence of the target stimulus on the detection object.

[0009] In one embodiment, determining the detection result of the detection object according to the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image includes:

[0010] Extracting first signal attenuation data of the target pixel from the first magnetic resonance image; and extracting second signal attenuation data of the target pixel from the second magnetic resonance image;

[0011] The detection result is determined based on a difference in a time dimension between the first signal attenuation data and the second signal attenuation data.

[0012] In one of the embodiments, the first signal attenuation data is characterized based on a first echo signal curve in a time dimension, and the second signal attenuation data is characterized based on a second echo signal curve in a time dimension;

[0013] The determining the detection result based on the difference between the first signal attenuation data and the second signal attenuation data in the time dimension includes:

[0014] For the same echo time, determining the detection result based on the distance between the first echo signal strength on the first echo signal curve and the second echo signal strength on the second echo signal curve;

[0015] The distance is positively correlated with the degree of influence of the target stimulus on the detection object.

[0016] In one embodiment, acquiring a first magnetic resonance data set of the detection object within a stimulation response time and a second magnetic resonance data set within a rest response time includes:

[0017] After applying target stimulation to the detection object for multiple times, the detection object is excited by radio frequency pulses to obtain first echo plane data of the detection object within the stimulation response time each time, so as to obtain the first magnetic resonance data set; and

[0018] Acquire second echo plane data of the detection object each time it is in the rest response time to obtain the second magnetic resonance data set.

[0019] In one embodiment, acquiring first plane echo data of the detection object each time it is in the stimulation response time and acquiring second plane echo data of the detection object each time it is in the rest response time includes:

[0020] After the detection object is excited by the radio frequency pulse, first echo plane data within the stimulus response time is acquired after a first delay time;

[0021] After acquiring the first plane echo data within the stimulation response time, acquiring the second plane echo data within the rest response time after a second delay time;

[0022] The first delay time represents the response time of the detection object to the target stimulus, and the second delay time represents the recovery time of the detection object to the target stimulus.

[0023] In one embodiment, the performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set respectively to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image of the detection object within the rest response time includes:

[0024] Filling the corresponding echo data set into the full sampling area in the K space; the K space includes the full sampling area and the under-sampling area, and the echo data set includes the first echo plane data or the second echo plane data;

[0025] Based on a preset regression model, predicting the under-sampled area to obtain a predicted echo data set for under-sampling of the echo data set;

[0026] Filling the predicted echo data set into the under-sampled region in the K space to obtain a synthetic K space;

[0027] Perform inverse Fourier transform on each echo data in the synthetic K space to obtain a reconstructed magnetic resonance image.

[0028] In one of the embodiments, the full sampling area is composed of a plurality of full sampling points, and the under-sampling area is composed of a plurality of under-sampling points;

[0029] The predicting of the under-sampled area based on a preset regression model to obtain a predicted echo data set for under-sampling of the echo data set includes:

[0030] In the K space, a mapping relationship between each of the under-sampling points and a corresponding adjacent full-sampling point is established based on the regression model;

[0031] Based on the mapping relationship, the predicted echo data corresponding to each of the under-sampling points is determined to obtain the predicted echo data set.

[0032] In a second aspect, the present application also provides a magnetic resonance scanning control device. The device comprises:

[0033] an acquisition module, configured to acquire, for a target stimulus applied to a detection object, a first magnetic resonance data set of the detection object within a stimulus response time and a second magnetic resonance data set within a rest response time;

[0034] a reconstruction module, configured to perform image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set respectively, to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image of the detection object within the rest response time;

[0035] A detection module is used to determine a detection result of the detection object based on the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the degree of influence of the target stimulus on the detection object.

[0036] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0037] In response to a target stimulus applied to a detection object, a first magnetic resonance data set of the detection object within a stimulus response time and a second magnetic resonance data set of the detection object within a rest response time are acquired;

[0038] Respectively performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image of the detection object within the rest response time;

[0039] A detection result of the detection object is determined according to a difference between target pixels in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the degree of influence of the target stimulus on the detection object.

[0040] In a fifth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0041] In response to a target stimulus applied to a detection object, a first magnetic resonance data set of the detection object within a stimulus response time and a second magnetic resonance data set of the detection object within a rest response time are acquired;

[0042] Respectively performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image of the detection object within the rest response time;

[0043] A detection result of the detection object is determined according to a difference between target pixels in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the degree of influence of the target stimulus on the detection object.

[0044] In a sixth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0045] In response to a target stimulus applied to a detection object, a first magnetic resonance data set of the detection object within a stimulus response time and a second magnetic resonance data set of the detection object within a rest response time are acquired;

[0046] Respectively performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image of the detection object within the rest response time;

[0047] A detection result of the detection object is determined according to a difference between target pixels in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the degree of influence of the target stimulus on the detection object.

[0048] The above-mentioned magnetic resonance scanning control method, device, computer equipment, storage medium and computer program product, on the one hand, respectively obtain a first magnetic resonance data set of the detection object within the stimulation response time and a second magnetic resonance data set within the resting response time, and then respectively perform image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image within the resting response time after being stimulated by the target, thereby being able to express the difference between the magnetic resonance images in two different states. Compared with the method of synthesizing a complete magnetic resonance image in the prior art, the imaging quality of the two types of magnetic resonance images and the accuracy of the content expressed by the magnetic resonance images are improved; on the other hand, by comparing the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image, the degree of influence of the target stimulation on the detection object can be determined, thereby ensuring the accuracy and feasibility of stimulation monitoring of the detection object, and providing more spatial information for subsequent research. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0050] Figure 1 It is a diagram showing an application environment of a magnetic resonance scanning control method according to an exemplary embodiment.

[0051] Figure 2 The figure is a flow chart of a magnetic resonance scanning control method according to an exemplary embodiment.

[0052] Figure 3 The present invention is a module diagram for acquiring echo-planar data according to an exemplary embodiment.

[0053] Figure 4 The present invention is a flowchart showing steps of performing image reconstruction processing on a magnetic resonance data set according to an exemplary embodiment.

[0054] Figure 5 The present invention is a flowchart showing a step of determining a detection result of a detection object according to an exemplary embodiment.

[0055] Figure 6 The present invention is a module diagram showing a method for determining a detection result according to an exemplary embodiment.

[0056] Figure 7 is a flow chart of a magnetic resonance scanning control method according to another exemplary embodiment.

[0057] Figure 8 It is a module diagram for collecting two-dimensional / three-dimensional EPI sequence data according to an exemplary embodiment.

[0058] Fig. 9 The figure is a block diagram of a magnetic resonance scanning control device according to an exemplary embodiment.

[0059] Fig.10 It is a block diagram of a computer for magnetic resonance scanning control according to an exemplary embodiment.

[0060] Fig.11 It is a block diagram of a computer-readable storage medium for magnetic resonance scanning control according to an exemplary embodiment.

[0061] Fig.12 It is a block diagram of a computer program product for magnetic resonance scanning control according to an exemplary embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0063] The term "and / or" in the embodiments of the present application refers to any and all possible combinations of one or more of the associated enumerated items. It should also be noted that when used in this specification, "include / comprise" specifies the presence of stated features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or components and / or their groups.

[0064] The terms "first", "second", etc. in this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0065] In addition, although the terms "first", "second", etc. are used many times in this application to describe various operations (or various elements or various applications or various instructions or various data), etc., these operations (or elements or applications or instructions or data) should not be limited by these terms. These terms are only used to distinguish one operation (or element or application or instruction or data) from another operation (or element or application or instruction or data). For example, a first magnetic resonance image can be called a second magnetic resonance image, and a second magnetic resonance image can also be called a first magnetic resonance image. It is just that the scopes included by the two are different, and it does not deviate from the scope of this application. The first magnetic resonance image and the second magnetic resonance image are both magnetic resonance image sets reconstructed based on magnetic resonance data collected within a specific time range, but the two are not magnetic resonance image sets reconstructed based on magnetic resonance data collected within the same specific time range.

[0066] The magnetic resonance scanning control method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a communication network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on a cloud or a network server.

[0067] In some embodiments, reference Figure 1, the server 104 first obtains a first magnetic resonance data set of the detection object within the stimulus response time and a second magnetic resonance data set within the rest response time for the target stimulus applied to the detection object; then, the server 104 performs image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set respectively to obtain a first magnetic resonance image of the detection object within the stimulus response time and a second magnetic resonance image within the rest response time; finally, the server 104 determines a detection result of the detection object based on the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the degree of influence of the target stimulus on the detection object.

[0068] In some embodiments, the terminal 102 (such as a mobile terminal, a fixed terminal) can be implemented in various forms. Among them, the terminal 102 can be a mobile terminal including a mobile phone, a smart phone, a laptop computer, a portable handheld device, a personal digital assistant (PDA), a tablet computer (PAD), etc., which can determine the detection result of the detection object according to the difference between the target pixels in the magnetic resonance image. The terminal 102 can also be a fixed terminal such as an automatic all-in-one machine, a digital TV, a desktop computer, a fixed computer, etc., which can determine the detection result of the detection object according to the difference between the target pixels in the magnetic resonance image.

[0069] In the following, it is assumed that the terminal 102 is a fixed terminal. However, it will be understood by those skilled in the art that the configuration according to the embodiments disclosed in the present application can also be applied to a mobile type terminal 102 if there are operations or elements specifically for mobile purposes.

[0070] In some embodiments, the data processing component running on the server 104 may load any of a variety of additional server applications and / or middle-tier applications being executed, such as HTTP (Hypertext Transfer Protocol), FTP (File Transfer Protocol), CGI (Common Gateway Interface), RDBMS (Relational Database Management System), etc.

[0071] In some embodiments, the server 104 may implement corresponding network functions through a deployed server (such as an independent server or a server cluster composed of multiple servers). The server 104 may also be suitable for running one or more application services or software components that provide the terminal 102 described in the above disclosure.

[0072] In some embodiments, the application service may include a service interface (magnetic resonance data acquisition interface, magnetic resonance image display interface, etc.) for providing users with magnetic resonance scanning control, as well as corresponding program services, etc. Among them, the software component may include, for example, an application program (SDK) or a client (APP) having the function of performing image reconstruction processing according to magnetic resonance data.

[0073] In some embodiments, the application or client provided by the server 104 with the function of performing image reconstruction processing based on magnetic resonance data includes a portal port that provides one-to-one application services to users in the foreground and multiple business systems that perform data processing in the background, so as to extend the application of the image reconstruction processing function based on magnetic resonance data to the APP or client, so that users can use and access the magnetic resonance scanning control function at any time and any place.

[0074] In some embodiments, the magnetic resonance scanning control function in the APP or client may be a computer program running in user mode to complete one or more specific tasks, which can interact with the user and has a visual user interface. The APP or client may include two parts: a graphical user interface (GUI) and an engine, which can provide a digital client system with multiple application services to the user in the form of a user interface.

[0075] In some embodiments, the user can input corresponding code data or control parameters to the APP or client through a preset input device or automatic control program to execute the application service of the computer program in the server 104 and display the application service in the user interface.

[0076] In some embodiments, the operating system running on the APP or client may include various versions of Microsoft Apple and / or Linux operating system, various commercial or similar Operating systems (including but not limited to various GNU / Linux operating systems, Google OS, etc.) and / or mobile operating systems such as Phone, OS, OS, The OS operating system, as well as other online operating systems or offline operating systems, are not specifically limited here.

[0077] In some embodiments, Figure 2 As shown, a magnetic resonance scanning control method is provided, and the method is applied to Figure 1Taking the server 104 in the example as an example, the method includes the following steps:

[0078] Step S11: for a target stimulus applied to the detection object, a first magnetic resonance data set of the detection object within a stimulus response time and a second magnetic resonance data set of the detection object within a rest response time are acquired.

[0079] In one embodiment, the detection object is a living object having neural elements, such as a human object or an animal object.

[0080] In one embodiment, the target stimulation is a physical stimulation or a biochemical stimulation acting on a neural element of a living subject, including optical stimulation, acoustic stimulation, drug stimulation, and the like.

[0081] Among them, after the target stimulation is applied to the detection object, the neural elements of the target stimulation will produce a stimulation response to enter a stimulation response state, and then gradually return to rest to enter a rest recovery state.

[0082] The time that the detected object is in the stimulus response state is the stimulus response time, and the time that the detected object is in the rest recovery state is the rest response time.

[0083] In some embodiments, the process of the server acquiring the first magnetic resonance data set and the second magnetic resonance data set may specifically include: after applying target stimulation to the detection object multiple times, using radio frequency pulses to excite the detection object, acquiring first plane echo data of the detection object during each stimulation response time to obtain the first magnetic resonance data set; and acquiring second plane echo data of the detection object during each rest response time to obtain the second magnetic resonance data set.

[0084] In order to study the spatial and temporal relationship of neural responses, target stimulation is applied to the test object multiple times to collect the plane echo data of the test object in the corresponding K space, thereby obtaining a magnetic resonance data set.

[0085] Among them, radio frequency pulse excitation of the detection object refers to sending an RF signal (Radio Frequency) of a certain amplitude, frequency, width and phase to the detection object, so that the neural elements in the detection object generate magnetic resonance signals (in this application, the magnetic resonance signal is the plane echo data), thereby obtaining a magnetic resonance data set.

[0086] In one embodiment, the server applies the same target stimulation and radio frequency pulse excitation to the detection object each time, and each time the plane echo data corresponding to the detection object is collected, the phase encoding trajectory for data collection in K space is changed to obtain the corresponding magnetic resonance data set.

[0087] Step S12: performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set respectively to obtain a first magnetic resonance image of the detection object in the stimulation response time and a second magnetic resonance image of the detection object in the rest response time.

[0088] Specifically, the server may perform data preprocessing, algorithm reconstruction processing, and image post-processing on the first magnetic resonance data set and the second magnetic resonance data set in sequence, so as to obtain the corresponding first magnetic resonance image and the second magnetic resonance image.

[0089] Among them, data preprocessing is used to preprocess the acquired magnetic resonance data, including removing noise, correcting scanner non-uniformity, correcting gradient drift, etc. These preprocessing steps help improve image quality and accuracy.

[0090] Among them, algorithmic reconstruction processing: using image reconstruction algorithm to convert K data into image domain to form the final magnetic resonance image. Among them, image reconstruction algorithms can include reconstruction methods based on sensitivity encoding (SENSE), reconstruction methods based on generalized autocalibrating partially parallel acquisitions (GRAPPA), reconstruction methods based on neural networks, reconstruction methods based on compressed sensing, etc. These algorithms restore images through mathematical models and image processing techniques according to the characteristics and sampling methods of magnetic resonance data.

[0091] Among them, image post-processing is used to post-process the reconstructed magnetic resonance images, including removing artifacts, enhancing contrast, smoothing images, etc. These post-processing steps help improve the visualization and diagnostic capabilities of the images.

[0092] Step S13: determining a detection result of the detection object according to a difference between target pixels in the first magnetic resonance image and the second magnetic resonance image.

[0093] In one embodiment, the pixel points in the first magnetic resonance image represent the magnetic resonance echo signal generated by a certain neural element on the living object during the stimulation response time. The pixel points in the second magnetic resonance image represent the magnetic resonance echo signal generated by a certain neural element on the living object during the resting response time.

[0094] The stimulation response time is the total stimulation response time between each stimulation response state of the neural element after each target stimulation and radio frequency pulse excitation are applied to the neural element. The resting response time is the total resting response time between each resting response state of the neural element after each target stimulation and radio frequency pulse excitation are applied to the neural element.

[0095] The time length corresponding to the total stimulus response time is the same as the time length corresponding to the total resting response time.

[0096] In some embodiments, the difference between the target pixel in the first magnetic resonance image and the second magnetic resonance image is the signal strength difference between the magnetic resonance echo signal for a certain neural element in the first magnetic resonance image and the magnetic resonance echo signal for a certain neural element in the second magnetic resonance image. Thus, the server can obtain the detection result of the detection object according to the signal strength difference.

[0097] Among them, the detection result represents the influence degree of the target stimulus on the detection object.

[0098] Specifically, if the target stimulus has a small impact on the detection object, it can be considered that the activity of the neural element corresponding to the target pixel point during the entire echo time (including the stimulus response time and the resting response time) is not affected by the target stimulus; if the target stimulus has a large impact on the detection object, it can be considered that the activity of the neural element corresponding to the target pixel point during the echo time has been affected by the target stimulus.

[0099] In the above-mentioned magnetic resonance scanning control process, the server obtains a first magnetic resonance data set of the detection object within the stimulation response time and a second magnetic resonance data set within the resting response time in response to the target stimulus applied to the detection object; image reconstruction processing is performed on the first magnetic resonance data set and the second magnetic resonance data set respectively to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image within the resting response time; based on the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image, a detection result of the detection object is determined; and the detection result represents the degree of influence of the target stimulus on the detection object. In this way, on the one hand, by respectively acquiring a first magnetic resonance data set of the detection object within the stimulation response time and a second magnetic resonance data set within the resting response time, and then performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set, respectively, a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image within the resting response time after being stimulated by the target are obtained, thereby being able to express the difference between the magnetic resonance images in two different states, and compared with the method of synthesizing a complete magnetic resonance image in the prior art, the imaging quality of the two types of magnetic resonance images and the accuracy of the content expressed by the magnetic resonance images are improved; on the other hand, by comparing the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image, the degree of influence of the target stimulation on the detection object can be determined, thereby ensuring the accuracy and feasibility of stimulation monitoring of the detection object, and providing more spatial information for subsequent research.

[0100] Those skilled in the art can understand that in the above method of the specific implementation, the disclosed method can be implemented in a more specific manner. For example, the above-described implementation in which the server determines the detection result of the detection object based on the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image is only illustrative.

[0101] Exemplarily, the manner in which the server performs image reconstruction processing on the first magnetic resonance data set; or the manner in which the server performs image reconstruction processing on the second magnetic resonance data set, etc., is merely a collection manner, and there may be other division manners in actual implementation, for example, the first magnetic resonance image of the first magnetic resonance data set and the second magnetic resonance image of the second magnetic resonance data set may be combined or may be collected into another system, or some features may be ignored or not executed.

[0102] In one embodiment, in step S11, the process of the server acquiring the first echo plane data and acquiring the second echo plane data can be specifically implemented in the following manner:

[0103] Step 1: After exciting the detection object with a radio frequency pulse, first echo plane data within the stimulus response time is acquired after a first delay time.

[0104] Step 2: After acquiring the first plane echo data within the stimulus response time, acquiring the second plane echo data within the rest response time after a second delay time.

[0105] Among them, the first delay time represents the response time of the detection object to the target stimulus, that is, the delay time from the detection object being stimulated to the stimulus response; the second delay time represents the recovery time of the detection object to the target stimulus, that is, the delay time of the detection object gradually recovering from the stimulus response state to the resting response state.

[0106] In an exemplary embodiment, see Figure 3 , Figure 3The module schematic diagram of an embodiment of acquiring plane echo data in the present application. Among them, module S1 represents that after the first target stimulation is performed on the detection object, the server performs the first radio frequency pulse excitation on the detection object at time T0, and then the detection object enters the stimulation response state after the first delay time from T0 to T1, so that the server collects the first plane echo data of the detection object within the stimulation response time from T1 to T2; then the detection object enters the resting response state after the second delay time from T2 to T3, so that the server collects the second plane echo data of the detection object within the resting response time from T3 to T4. Correspondingly, after time T4, the server sequentially acquires the second plane echo data, the third plane echo data, and so on for the detection object, i.e., module S2, module S3... (not shown in the figure); until the server acquires the nth plane echo data for the detection object for the last time, refer to module Sn, wherein module Sn represents that after the nth target stimulation is performed on the detection object, the server performs the nth radio frequency pulse excitation on the detection object at time Tn-4, and then the detection object enters the stimulation response state after the first delay time from Tn-4 to Tn-3, so that the server collects the first plane echo data of the detection object within the stimulation response time from Tn-3 to Tn-2; then the detection object enters the resting response state after the second delay time from Tn-2 to Tn-1, so that the server collects the second plane echo data of the detection object within the resting response time from Tn-1 to Tn.

[0107] In an exemplary embodiment, see Figure 4 , Figure 4 This is a flow chart of an embodiment of image reconstruction processing of a magnetic resonance data set in the present application. In step S12, the server performs image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set respectively to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image within the rest response time. Specifically, the following technical contents may be performed:

[0108] Step a1: Fill the corresponding echo data set into the full sampling area in the K space.

[0109] In one embodiment, the echo data set includes first echo plane data or second echo plane data. That is, if the first magnetic resonance data set is subjected to image reconstruction processing, the first echo plane data in the first magnetic resonance data set is filled into the full sampling area in the K space in step a1. Conversely, if the second magnetic resonance data set is subjected to image reconstruction processing, the second echo plane data in the second magnetic resonance data set is filled into the full sampling area in the K space in step a1.

[0110] K-space is a two-dimensional or three-dimensional data space, in which each point corresponds to a sampling point in the magnetic resonance (MR) scanning process. That is, the K-space of magnetic resonance data refers to a space used to represent the collected raw data in magnetic resonance imaging.

[0111] In magnetic resonance imaging, the change of gradient magnetic field can make the nuclear spins at different positions produce different resonance frequencies. By collecting the signals of these resonance frequencies, a K-space data set can be obtained. The coordinate axis of K-space represents different spatial frequencies, where the low-frequency component is located at the center of K-space and the high-frequency component is located at the edge of K-space.

[0112] In one embodiment, the K space includes a full sampling region and an under-sampling region, wherein the full sampling region is composed of a plurality of full sampling points, and the under-sampling region is composed of a plurality of under-sampling points.

[0113] Specifically, the sampling methods of K space can be divided into full sampling and under sampling. Among them, the full sampling area refers to the complete collection of all data points in the K space, that is, a complete scan of the K space. Full sampling can obtain high-quality images, but requires a longer scanning time. In full sampling, every point in the K space is sampled, and complete frequency information can be obtained, thereby achieving high spatial resolution and image contrast. Among them, the under sampling area refers to the collection of only some data points in the K space, that is, an incomplete scan of the K space. Under sampling can reduce the scanning time and increase the imaging speed, but it will lead to reduced image quality and the generation of artifacts.

[0114] Step a2: Based on a preset regression model, the under-sampled area is predicted to obtain a predicted echo data set for the under-sampled echo data set.

[0115] In some embodiments, the server predicts the undersampled area including the following steps:

[0116] Step 1: In K space, a mapping relationship between each under-sampled point and the corresponding adjacent fully sampled point is established based on the regression model.

[0117] In one embodiment, the server first obtains the data filling relationship in a two-dimensional case or a three-dimensional case based on GRAPPA algorithm training, that is, obtains a first-type regression model for a two-dimensional plane or a second-type regression model for a three-dimensional space. Then, the server predicts the weight coefficient of each under-sampled point in the K space based on the first-type regression model or the second-type regression model to determine the mapping relationship between the under-sampled point and the corresponding adjacent full-sampled point.

[0118] Among them, the first type of regression model can be characterized based on the following formula:

[0119]

[0120] Among them, S kx，ky，t are the undersampled points on each two-dimensional plane in k-space, are the full sampling points adjacent to each under-sampling point on each two-dimensional plane in k-space, The weight coefficient of the under-sampling point is predicted based on the adjacent full-sampling points on each two-dimensional plane in the k-space.

[0121] Among them, the second type of regression model can be characterized based on the following formula:

[0122]

[0123] Among them, S kx，ky，kz，t is the under-sampled point in the three-dimensional space where the k-space is located, is the full sampling point adjacent to each under-sampling point in the three-dimensional space where the k-space is located, The weight coefficient of the under-sampling point is predicted based on the adjacent full-sampling points in the three-dimensional space where the k-space is located.

[0124] Step 2: Based on the mapping relationship, the predicted echo data corresponding to each under-sampling point is determined to obtain a predicted echo data set.

[0125] Specifically, the S predicted by the first type of regression model kx，ky，t As the predicted echo data of each under-sampling point in the two-dimensional plane of k-space, or, the S predicted by the second type regression model kx，ky，kz，t As the predicted echo data of each under-sampling point in the three-dimensional space where the k-space is located, a predicted echo data set is obtained.

[0126] Step a3: Fill the predicted echo data set into the under-sampled region in the K space to obtain a synthetic K space.

[0127] Specifically, the predicted echo data for the under-sampling points are used as image space data to fill the under-sampling region in the K space, so that the data of each region fills the complete K space, that is, the synthetic K space.

[0128] Step a4: Perform inverse Fourier transform on each echo data in the synthetic K space to obtain a reconstructed magnetic resonance image.

[0129] Specifically, each echo data in the synthetic K space (including the echo data of the full sampling points and the echo data of the under-sampling points) is subjected to inverse Fourier transformation to obtain a reconstructed magnetic resonance image. In addition to reconstructing the image by inverse Fourier transformation, image reconstruction can also be achieved by a filtered back projection algorithm, an iterative reconstruction algorithm, etc., which is not specifically limited here.

[0130] In an exemplary embodiment, see Figure 5 , Figure 5 This is a flow chart of an embodiment of determining the detection result of the detection object in the present application. In step S13, the server determines the detection result of the detection object according to the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image, and specifically, the following technical contents can be performed:

[0131] Step b1: extracting first signal attenuation data of a target pixel from the first magnetic resonance image; and extracting second signal attenuation data of a target pixel from the second magnetic resonance image.

[0132] In one embodiment, the first signal attenuation data represents all magnetic resonance echo signals generated by the target pixel corresponding to the target pixel during each stimulation response time after the target stimulus and the radio frequency pulse are applied. The second signal attenuation data represents all magnetic resonance echo signals generated by the target pixel corresponding to the target pixel during each rest response time after the target stimulus and the radio frequency pulse are applied.

[0133] The total time length corresponding to each stimulation response time is the same as the total time length corresponding to each rest response time.

[0134] In one embodiment, the first signal attenuation data is characterized based on the first echo signal curve in the time dimension, and the second signal attenuation data is characterized based on the second echo signal curve in the time dimension.

[0135] The time dimension is the echo time dimension, which includes the total stimulus response time for the first magnetic resonance image and the total resting response time for the second magnetic resonance image.

[0136] Step b2: Determine a detection result based on the difference between the first signal attenuation data and the second signal attenuation data in the time dimension.

[0137] In one embodiment, the server determining the detection result may specifically include: determining the detection result based on a distance between a first echo signal strength on a first echo signal curve and a second echo signal strength on a second echo signal curve for the same echo time.

[0138] Among them, the distance is positively correlated with the degree of influence of the target stimulation on the detection object, that is, at the same echo time, if the distance between the first echo signal intensity on the first echo signal curve and the second echo signal intensity on the second echo signal curve is larger, it indicates that the activity of the corresponding neural element in the detection object is more affected by the target stimulation; if the distance between the first echo signal intensity on the first echo signal curve and the second echo signal intensity on the second echo signal curve is smaller, it indicates that the activity of the corresponding neural element in the detection object is less affected by the target stimulation.

[0139] In an exemplary embodiment, see Figure 6 , Figure 6 This is a schematic diagram of a module for determining an embodiment of the detection result in the present application. Among them, the server applies target stimulation to the neural elements in the detection object at time T1, and then stimulates the neural elements by radio frequency pulses in the T1-T2 time interval, and then the neural elements enter the stimulation response state or the resting response state at time T3, so that the server collects the first echo signal curve M1 of the neural elements in the stimulation response state and the second echo signal curve M2 of the neural elements in the resting response state. As shown in the figure, the horizontal axis of the curve graph represents the echo time, and the vertical axis represents the echo signal intensity. Among them, when the echo time reaches T4 time, the first echo signal curve M1 and the second echo signal curve M2 begin to show obvious differences, that is, the distance between the first echo signal intensity on the first echo signal curve M1 and the second echo signal intensity on the second echo signal curve M2 has exceeded the preset distance, so it can be considered that the activity of the neural elements has been affected by the target stimulation since time T4.

[0140] In order to more clearly illustrate the magnetic resonance scanning control method provided by the embodiment of the present disclosure, the magnetic resonance scanning control method is specifically described below with reference to a specific embodiment. Figure 7 , Figure 7 FIG. 1 is a flow chart of a magnetic resonance scanning control method according to another exemplary embodiment. The magnetic resonance scanning control method is used in the server 104 and specifically includes the following contents:

[0141] Step S21: Determine the configuration of the stimulation.

[0142] Specifically, it includes configuring the stimulation type, stimulation delay time, stimulation site, stimulation size, etc.

[0143] The stimulation is external stimulation, including optical stimulation, acoustic stimulation (flashing light on the part, etc.).

[0144] Step S22: Apply the first stimulation to the human body according to the stimulation configuration.

[0145] Step S23: After the first stimulation, a first RF pulse is applied to the human body.

[0146] Step S24: After a first preset delay time has passed, the multi-channel coil is used to collect data on the human body for the first time.

[0147] Step S25: After a second preset delay time, a second data collection is performed on the human body through the multi-channel coil.

[0148] Among them, the above-mentioned first data acquisition or second data acquisition can be the EPI sequence data of multiple two-dimensional plane layers of the human body in the K space acquired by the server through a multi-channel coil, or it can be the EPI sequence data of the entire three-dimensional space of the human body in the K space acquired by the server through a multi-channel coil.

[0149] Each point in the K space corresponds to a sampling point in the magnetic resonance scanning (ie, data acquisition) process.

[0150] In an exemplary embodiment, see Figure 8 , Figure 8 This is a module diagram of an embodiment of collecting two-dimensional / three-dimensional EPI sequence data in the present application. Among them, Figure P1 corresponds to the process of collecting two-dimensional EPI sequence data, and its phase encoding is only distributed in one gradient axis, and Figure P2 corresponds to the process of collecting three-dimensional EPI sequence data, and phase encoding is also applied in the layer direction. Among them, the coordinate axis Gz represents the layer selection gradient of data acquisition, the coordinate axis Gx represents the frequency encoding gradient of data acquisition, the coordinate axis Gy represents the phase encoding gradient of data acquisition, and RF represents the RF pulse applied to the human body. In this embodiment, multiple echo signals can be collected after each radio frequency (RF) excitation, that is, after a single radio frequency excitation, signals of multiple echo times of the same pixel / voxel can be obtained.

[0151] Step S26: Repeat the above process multiple times to obtain two types of downsampled multi-echo EPI data after multiple excitations.

[0152] Among them, in a complete magnetic resonance scanning control experiment, the stimulation applied to the human body each time is the same, the RF pulse applied to the human body each time is also the same, and the acquisition time and delay time each time are also the same.

[0153] The two types of downsampled multi-echo EPI data include: a first type of multi-echo EPI data including changes in neural activity caused by stimulation, and a second type of multi-echo EPI data including changes in neural activity not caused by stimulation.

[0154] Each time the above process is repeated, the phase encoding trajectory for data acquisition in the K-space is changed, that is, EPI data of different K-space regions are acquired.

[0155] Among them, changing the phase encoding trajectory is to design the Gy trajectory of the two-dimensional / three-dimensional EPI sequence data in the K space, so that the data acquisition area in the y direction in the K space can be determined based on the Gy trajectory.

[0156] Among them, the data acquisition process is the process of reading the corresponding data values ​​back and forth in the Kx axis and Ky axis of the K space. The area of ​​Gy in the corresponding direction is to jump to the position at the corresponding distance on the y axis of the K space for reading. That is, if you want to achieve uniform and regular down-sampling, it is achieved by setting the direction and area of ​​Gy. For example, in the two-dimensional plane of the K space, every time you collect data at an X distance, you can collect data uniformly and regularly, that is, collect data at the first X distance, the second X distance, the third X distance, and so on.

[0157] Step S27: reconstructing the first type of multi-echo EPI data to obtain a first type of multi-echo image; and reconstructing the second type of multi-echo EPI data to obtain a second type of multi-echo image.

[0158] Wherein, step S27 specifically includes the following steps:

[0159] Step 7.1: Pre-collect a small amount of data at the center of the k-echo space as calibration data.

[0160] The small amount of data belongs to the first type of multi-echo EPI data or the second type of multi-echo EPI data collected in the K-echo space, and the small amount of data is used as a known point in the K-echo space.

[0161] Step 7.2: Based on the GRAPPA algorithm and calibration data, the weights of each unknown point in the k-echo space are predicted.

[0162] The unknown points are under-sampling points in the process of down-sampling the multi-echo EPI data.

[0163] Among them, the GRAPPA algorithm for the first type of multi-echo EPI data can be characterized based on the following formula:

[0164]

[0165] Among them, S kx，ky，t is an unknown point in the k-echo space, are the known points (i.e. calibration data) corresponding to the unknown points on each two-dimensional plane in the k-echo space, The weight coefficient of the unknown point is predicted based on the adjacent known points on each two-dimensional plane in the k-echo space.

[0166] Among them, the GRAPPA algorithm for the second type of multi-echo EPI data can be characterized based on the following formula:

[0167]

[0168] Among them, S kx，ky，kz，t is an unknown point in k-echo space, is the known point adjacent to the unknown point in the three-dimensional space where the k-echo space is located, The weight coefficient of the unknown point is predicted based on the adjacent known points in the three-dimensional space where the k-echo space is located.

[0169] Step 7.3: According to the preset data filling relationship in two-dimensional / three-dimensional cases, the EPI data of the unknown point is calculated through the EPI data of the known points around the unknown point and the weight coefficient of the predicted unknown point.

[0170] Among them, the EPI data of the unknown point is the S calculated based on the GRAPPA algorithm. kx，ky，t or S kx，ky，kz，t .

[0171] Step 7.4 uses the calculated EPI data of the unknown point to perform convolution filling on the downsampled k-echo space data.

[0172] Among them, convolution filling is a repeated filling process. For example, the first acquisition is to obtain the RPI data of certain points in the 4x4 square. The second, third... times are also to obtain the RPI data of the certain points in the subsequent 4x4 squares. Filling the data is also to repeatedly fill in several fixed uncollected points in each square.

[0173] Step 7.5: Perform inverse Fourier transform on each EPI data in the filled k-echo space to obtain the corresponding reconstructed multi-echo EPI image.

[0174] Step S28: comparing the difference between the first type of multi-echo image and the second type of multi-echo image to determine the response generated by the stimulus.

[0175] When the stimulus response caused by the applied stimulus reaches the neurons of the human body, the neurons enter an active state, so that the change in the magnetic susceptibility of the neurons causes the change in the attenuation of the neural signals emitted by the neurons. The attenuation of the neural signals is characterized by the change curve of the RPI data of the pixel points on the multi-echo image during the echo time of the entire stimulus response.

[0176] Specifically, if the same pixel point in two sets of images (based on the first type of multi-echo images that include the influence of stimulation on neural activity and the second type of multi-echo images that do not include the influence of stimulation on neural activity) has a difference in the change curve within the echo time of the entire stimulation response, it proves that the activity of the neuron corresponding to the pixel point responds to the stimulation; if the same pixel point in the two sets of images has no difference in the change curve within the echo time of the entire stimulation response (or the difference is considered to be negligible), it proves that the activity of the neuron corresponding to the pixel point will not respond to the stimulation.

[0177] In this way, on the one hand, by respectively acquiring a first magnetic resonance data set of the detection object within the stimulation response time and a second magnetic resonance data set within the resting response time, and then performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set, respectively, a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image within the resting response time after being stimulated by the target are obtained, thereby being able to express the difference between the magnetic resonance images in two different states, and compared with the method of synthesizing a complete magnetic resonance image in the prior art, the imaging quality of the two types of magnetic resonance images and the accuracy of the content expressed by the magnetic resonance images are improved; on the other hand, by comparing the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image, the degree of influence of the target stimulation on the detection object can be determined, thereby ensuring the accuracy and feasibility of stimulation monitoring of the detection object, and providing more spatial information for subsequent research.

[0178] It should be understood that although Figure 2-Figure 8 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2-Figure 8 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0179] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can refer to each other, and each embodiment focuses on the differences from other embodiments. For related points, please refer to the description of other method embodiments.

[0180] Fig. 9 is a block diagram of a magnetic resonance scanning control device provided in an embodiment of the present application. Fig. 9The magnetic resonance scanning control device 10 includes: an acquisition module 11, a reconstruction module 12, and a detection module 13.

[0181] The acquisition module 11 is used to acquire a first magnetic resonance data set of the detection object in a stimulation response time and a second magnetic resonance data set in a rest response time in response to a target stimulus applied to the detection object;

[0182] The reconstruction module 12 is used to perform image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set respectively to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image within the rest response time;

[0183] The detection module 13 is used to determine the detection result of the detection object according to the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the influence degree of the target stimulus on the detection object.

[0184] In some embodiments, in the aspect of determining the detection result of the detection object according to the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image, the magnetic resonance scanning control device 10 further includes:

[0185] Extracting first signal attenuation data of the target pixel from the first magnetic resonance image; and extracting second signal attenuation data of the target pixel from the second magnetic resonance image;

[0186] The detection result is determined based on a difference in a time dimension between the first signal attenuation data and the second signal attenuation data.

[0187] In some embodiments, the first signal attenuation data is characterized based on a first echo signal curve in a time dimension, and the second signal attenuation data is characterized based on a second echo signal curve in a time dimension;

[0188] In the aspect of determining the detection result based on the difference between the first signal attenuation data and the second signal attenuation data in the time dimension, the magnetic resonance scanning control device 10 further includes:

[0189] For the same echo time, determining the detection result based on the distance between the first echo signal strength on the first echo signal curve and the second echo signal strength on the second echo signal curve;

[0190] The distance is positively correlated with the degree of influence of the target stimulus on the detection object.

[0191] In some embodiments, in the aspect of acquiring a first magnetic resonance data set of the detection object within the stimulation response time and a second magnetic resonance data set within the rest response time, the magnetic resonance scanning control device 10 further includes:

[0192] After applying target stimulation to the detection object for multiple times, the detection object is excited by radio frequency pulses to obtain first echo plane data of the detection object within the stimulation response time each time, so as to obtain the first magnetic resonance data set; and

[0193] Acquire second echo plane data of the detection object each time it is in the rest response time to obtain the second magnetic resonance data set.

[0194] In some embodiments, in terms of acquiring first plane echo data of the detection object each time it is in the stimulation response time and acquiring second plane echo data of the detection object each time it is in the rest response time, the magnetic resonance scanning control device 10 further includes:

[0195] After the detection object is excited by the radio frequency pulse, first echo plane data within the stimulus response time is acquired after a first delay time;

[0196] After acquiring the first plane echo data within the stimulation response time, acquiring the second plane echo data within the rest response time after a second delay time;

[0197] The first delay time represents the response time of the detection object to the target stimulus, and the second delay time represents the recovery time of the detection object to the target stimulus.

[0198] In some embodiments, in the aspect of performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set respectively to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image of the detection object within the rest response time, the magnetic resonance scanning control device 10 further includes:

[0199] Filling the corresponding echo data set into the full sampling area in the K space; the K space includes the full sampling area and the under-sampling area, and the echo data set includes the first echo plane data or the second echo plane data;

[0200] Based on a preset regression model, predicting the under-sampled area to obtain a predicted echo data set for under-sampling of the echo data set;

[0201] Filling the predicted echo data set into the under-sampled region in the K space to obtain a synthetic K space;

[0202] Perform inverse Fourier transform on each echo data in the synthetic K space to obtain a reconstructed magnetic resonance image.

[0203] In some embodiments, the full sampling area is composed of a plurality of full sampling points, and the under-sampling area is composed of a plurality of under-sampling points;

[0204] In the aspect of predicting the under-sampled region based on a preset regression model to obtain a predicted echo data set for under-sampling of the echo data set, the magnetic resonance scanning control device 10 further includes:

[0205] In the K space, a mapping relationship between each of the under-sampling points and a corresponding adjacent full-sampling point is established based on the regression model;

[0206] Based on the mapping relationship, the predicted echo data corresponding to each of the under-sampling points is determined to obtain the predicted echo data set.

[0207] Fig.10 2 is a block diagram of a computer device 20 provided in an embodiment of the present application. For example, the computer device 20 may be an electronic device, an electronic component, or a server array, etc. Fig.10 The computer device 20 includes a processor 21, which may be a processor set, which may include one or more processors, and the computer device 20 includes a memory resource represented by a memory 22, wherein a computer program, such as an application, is stored on the memory 22. The computer program stored in the memory 22 may include one or more modules, each corresponding to a set of executable instructions. In addition, the processing component 21 is configured to implement the magnetic resonance scanning control method as described above when executing the computer program.

[0208] In some embodiments, the computer device 20 is an electronic device in which a computing system can run one or more operating systems, including any operating system discussed above and any commercial server operating system. The computer device 20 can also run any of a variety of additional server applications and / or middle-tier applications, including HTTP (Hypertext Transfer Protocol) servers, FTP (File Transfer Protocol) servers, CGI (Common Gateway Interface) servers, super servers, database servers, etc. Exemplary database servers include, but are not limited to, database servers commercially available from (International Business Machines) and the like.

[0209] In some embodiments, the processing component 21 generally controls the overall operation of the computer device 20, such as operations associated with display, data processing, data communication, and recording operations. The processor 21 may include one or more processor components to execute a computer program to complete all or part of the steps of the above-mentioned method. In addition, the processor component may include one or more modules to facilitate the interaction between the processor component and other components. For example, the processor component may include a multimedia module to facilitate the use of the multimedia component to control the interaction between the user computer device 20 and the processor 21.

[0210] In some embodiments, the processor component in the processor 21 may also be referred to as a CPU (Central Processing Unit). The processor component may be an electronic chip having the ability to process signals. The processor may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor component, etc. In addition, the processor component may be implemented by an integrated circuit chip.

[0211] In some embodiments, the memory 22 is configured to store various types of data to support operations on the computer device 20. Examples of such data include instructions for any application or method operating on the computer device 20, collected data, messages, pictures, videos, etc. The memory 22 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, optical disk, or graphene memory.

[0212] In some embodiments, the memory 22 can be a memory stick, a TF card, etc., which can store all the information in the computer device 20, including the input raw data, computer programs, intermediate operation results and final operation results are all stored in the memory 22. In some embodiments, it stores and retrieves information according to the location specified by the processor. In some embodiments, only with the memory 22 can the computer device 20 have a memory function to ensure normal operation. In some embodiments, the memory 22 of the computer device 20 can be divided into main memory (internal memory) and auxiliary memory (external memory) according to its purpose, and there is also a classification method of dividing it into external memory and internal memory. External memory is usually a magnetic medium or an optical disk, etc., which can store information for a long time. Memory refers to the storage component on the motherboard, which is used to store the data and programs currently being executed, but is only used to temporarily store programs and data. If the power is turned off or the power is cut off, the data will be lost.

[0213] In some embodiments, the computer device 20 may further include: a power supply component 23 configured to perform power management of the computer device 20, a wired or wireless network interface 24 configured to connect the computer device 20 to a network, and an input / output (I / O) interface 25. The computer device 20 may operate based on an operating system stored in the memory 22, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD or the like.

[0214] In some embodiments, power supply assembly 23 provides power to various components of computer device 20. Power supply assembly 23 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to computer device 20.

[0215] In some embodiments, the wired or wireless network interface 24 is configured to facilitate wired or wireless communication between the computer device 20 and other devices. The computer device 20 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof.

[0216] In some embodiments, the wired or wireless network interface 24 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the wired or wireless network interface 24 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0217] In some embodiments, the input / output (I / O) interface 25 provides an interface between the processor 21 and a peripheral interface module, which may be a keyboard, a click wheel, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0218] Fig.11 3 is a block diagram of a computer-readable storage medium 30 provided in an embodiment of the present application. The computer-readable storage medium 30 stores a computer program 31, wherein the computer program 31 implements the above-mentioned magnetic resonance scanning control method when executed by a processor.

[0219] If the integrated units of the functional units in the various embodiments of the present application are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium 30. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer-readable storage medium 30 includes several instructions in a computer program 31 to enable a computer device (which can be a personal computer, a system server, or a network device, etc.), an electronic device (such as MP3, MP4, etc., or a smart terminal such as a mobile phone, a tablet computer, a wearable device, or a desktop computer, etc.) or a processor to execute all or part of the steps of the methods of various implementation methods of the present application.

[0220] Fig.12 4 is a block diagram of a computer program product 40 provided in an embodiment of the present application. The computer program product 40 includes program instructions 41, and the program instructions 41 can be executed by a processor of the server 20 to implement the magnetic resonance scanning control method as described above.

[0221] Those skilled in the art should understand that the embodiments of the present application may provide a magnetic resonance scanning control method, a magnetic resonance scanning control device 10, a computer device 20, a computer-readable storage medium 30, or a computer program product 40. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product 40 implemented on one or more computer program instructions 41 (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0222] The present application is described with reference to the flowchart and / or block diagram of the magnetic resonance scanning control method, magnetic resonance scanning control device 10, computer device 20, computer readable storage medium 30 or computer program product 40 according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by the computer program product 40. These computer program products 40 can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the program instructions 41 executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0223] These computer program products 40 may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the program instructions 41 stored in the computer program product 40 produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0224] These program instructions 41 may also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the program instructions 41 executed on the computer or other programmable device for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0225] It should be noted that the above-mentioned various methods, devices, electronic devices, computer-readable storage media, computer program products, etc. may also include other implementation methods according to the description of the method embodiments. The specific implementation methods can refer to the description of the relevant method embodiments, and will not be described one by one here.

[0226] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0227] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A magnetic resonance scanning control method, characterized in that: The method comprises: In response to a target stimulus applied to a detection object, a first magnetic resonance data set of the detection object within a stimulus response time and a second magnetic resonance data set of the detection object within a rest response time are acquired; Respectively performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image of the detection object within the rest response time; A detection result of the detection object is determined according to a difference between target pixels in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the degree of influence of the target stimulus on the detection object.

2. The method according to claim 1, characterized in that The step of determining the detection result of the detection object according to the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image includes: Extracting first signal attenuation data of the target pixel from the first magnetic resonance image; and extracting second signal attenuation data of the target pixel from the second magnetic resonance image; The detection result is determined based on a difference in a time dimension between the first signal attenuation data and the second signal attenuation data.

3. The method according to claim 2, characterized in that The first signal attenuation data is characterized based on a first echo signal curve in a time dimension, and the second signal attenuation data is characterized based on a second echo signal curve in a time dimension; The determining the detection result based on the difference between the first signal attenuation data and the second signal attenuation data in the time dimension includes: For the same echo time, determining the detection result based on the distance between the first echo signal strength on the first echo signal curve and the second echo signal strength on the second echo signal curve; The distance is positively correlated with the degree of influence of the target stimulus on the detection object.

4. The method according to claim 1, characterized in that: The acquiring of a first magnetic resonance data set of the detection object within a stimulation response time and a second magnetic resonance data set within a rest response time comprises: After applying target stimulation to the detection object for multiple times, the detection object is excited by radio frequency pulses to obtain first echo plane data of the detection object within the stimulation response time each time, so as to obtain the first magnetic resonance data set; and Acquire second echo plane data of the detection object each time it is in the rest response time to obtain the second magnetic resonance data set.

5. The method according to claim 4, characterized in that Acquiring first plane echo data of the detection object each time it is in the stimulation response time and acquiring second plane echo data of the detection object each time it is in the rest response time, including: After the detection object is excited by the radio frequency pulse, first echo plane data within the stimulus response time is acquired after a first delay time; After acquiring the first plane echo data within the stimulation response time, acquiring the second plane echo data within the rest response time after a second delay time; The first delay time represents the response time of the detection object to the target stimulus, and the second delay time represents the recovery time of the detection object to the target stimulus.

6. The method according to claim 4, characterized in that The performing image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set respectively to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image within the rest response time includes: Filling the corresponding echo data set into the full sampling area in the K space; the K space includes the full sampling area and the under-sampling area, and the echo data set includes the first echo plane data or the second echo plane data; Based on a preset regression model, predicting the under-sampled area to obtain a predicted echo data set for under-sampling of the echo data set; Filling the predicted echo data set into the under-sampled region in the K space to obtain a synthetic K space; Perform inverse Fourier transform on each echo data in the synthetic K space to obtain a reconstructed magnetic resonance image.

7. The method according to claim 6, characterized in that The full sampling area is composed of a plurality of full sampling points, and the under-sampling area is composed of a plurality of under-sampling points; The predicting of the under-sampled area based on a preset regression model to obtain a predicted echo data set for under-sampling of the echo data set includes: In the K space, a mapping relationship between each of the under-sampling points and a corresponding adjacent full-sampling point is established based on the regression model; Based on the mapping relationship, the predicted echo data corresponding to each of the under-sampling points is determined to obtain the predicted echo data set.

8. A magnetic resonance scanning control device, characterized in that: The device comprises: an acquisition module, configured to acquire, for a target stimulus applied to a detection object, a first magnetic resonance data set of the detection object within a stimulus response time and a second magnetic resonance data set within a rest response time; a reconstruction module, configured to perform image reconstruction processing on the first magnetic resonance data set and the second magnetic resonance data set respectively, to obtain a first magnetic resonance image of the detection object within the stimulation response time and a second magnetic resonance image of the detection object within the rest response time; A detection module is used to determine a detection result of the detection object based on the difference between the target pixel points in the first magnetic resonance image and the second magnetic resonance image; the detection result represents the degree of influence of the target stimulus on the detection object.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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