Method and device for detecting magnetic resonance brain image signal loss

Abstract

The invention relates to a magnetic resonance brain image signal loss detection method, which comprises the following steps of: acquiring a magnetic resonance brain image signal, and preprocessing the magnetic resonance brain image signal to obtain a brain image; respectively extracting image features of the brain image and the prior template in the same mode; and comparing the brain image with the image feature of the prior template, and judging whether the magnetic resonance brain image signal is missing or not. The invention further provides a corresponding detection device. According to the method, on the premise that the quality sensitivity of the input image is reduced, fewer system computing resources are occupied and higher accuracy is provided, whether the magnetic resonance brain image signal is missing or not is judged through the standard quantified index, and therefore the automation level is improved.

Description

Technical Field

[0001] The present application relates to a technical field related to magnetic resonance brain image signal processing, and in particular to a method and device for detecting missing magnetic resonance brain image signals. Background Art

[0002] Magnetic resonance imaging technology has the advantages of being non-invasive and having high spatial resolution, and is widely used in clinical and scientific research. Good brain image quality is the prerequisite for data analysis and processing. However, during data acquisition, it is easy to cause signal loss due to improper operation, such as incomplete scanning of the skull vertex and cerebellum or machine failure. Detection of signal loss can effectively help scanners judge the quality of brain images and thus optimize the scanning plan.

[0003] At present, the detection of missing signals in magnetic resonance brain imaging still mainly relies on naked eye observation and empirical judgment. It can also be judged whether there is a problem of missing signals by detecting signal strength, noise level, etc. The existing technology may have the following disadvantages:

[0004] On the one hand, detection based on visual observation and empirical judgment lacks quantitative standardized indicators due to subjective differences among scanners. Also, due to the limitations of manual work such as attention and fatigue, there may be problems such as misjudgment and omissions, and the degree of automation is not high.

[0005] On the other hand, the accuracy of judging by detecting signal strength, noise level, etc. depends on the quality of the input image data itself. In the case of poor image input quality, it may not be possible to accurately identify signal loss. Moreover, since such methods often require a large amount of computing resources for detection after obtaining signal data, they often cause detection delays and reduced efficiency in actual applications.

[0006] Therefore, in the context of low accuracy, low degree of automation, lack of standard quantitative indicators, and high requirements for computing resources and image quality in existing technologies, how to reduce the sensitivity of input image quality, occupy less system computing resources, and provide higher accuracy while using standard quantitative indicators to determine whether magnetic resonance brain imaging signals are missing, thereby improving the level of automation, is a technical problem that needs to be solved. Summary of the invention

[0007] In view of the above problems in the prior art, the present application provides a method and device for detecting the absence of magnetic resonance brain imaging signals, so as to reduce the sensitivity of input image quality, occupy less system computing resources, and provide higher accuracy, and determine whether the magnetic resonance brain imaging signals are missing through standard quantitative indicators, thereby improving the level of automation.

[0008] To achieve the above-mentioned purpose, the present application provides a first aspect of a method for detecting missing magnetic resonance brain imaging signals, comprising:

[0009] Acquire magnetic resonance brain imaging signals and perform preprocessing to obtain brain imaging images;

[0010] Extracting the image features of the brain image and the prior template respectively in the same manner;

[0011] The brain image is compared with the image features of the prior template to determine whether the magnetic resonance brain image signal is missing.

[0012] From the above, by comparing the features of the MRI brain image signal with the prior template, there is no need to require a high input image quality; by comparing the features of the MRI brain image, there is no need to perform operations such as spectrum conversion, so the operation is simple and the accuracy is good; the prior template can be calculated in advance, so the amount of calculation is reduced; calculating the image features of the scanned image and the prior template by the same method ensures the consistency of the data and improves the accuracy. Quantitative evaluation and automated operation can be achieved through indicator calculation.

[0013] As a possible implementation of the first aspect, the extracting of image features includes calculating the number of voxels, sagittal, coronal, and transverse areas;

[0014] From the above, it can be seen that by comparing the difference between the number of voxels and the standard space prior template, it is relatively simple and quick; by improving the recognition accuracy of key parts, the efficiency is improved.

[0015] As a possible implementation manner of the first aspect, the sagittal, coronal, and transverse areas are calculated by a maximum intensity projection method.

[0016] From the above, the area of ​​the two-dimensional image is calculated by the maximum density projection method, which simplifies the complex three-dimensional data set.

[0017] As a possible implementation of the first aspect, the image features of the brain image are compared with those of the prior template by calculating the number of voxels of the brain image and the percentage of the sagittal, coronal and transverse areas of the brain image to the prior template; the determination of whether the magnetic resonance brain image signal is missing is evaluated based on the size of the percentage, and the scanning plan is adjusted based on the evaluation result.

[0018] From the above, percentage calculation can be used as a quantitative indicator to quickly output the detection results, which improves efficiency and reduces computing resource usage.

[0019] As a possible implementation manner of the first aspect, the preprocessing includes:

[0020] Converting the acquired magnetic resonance brain image signal in a non-NIfTI file format into a NIfTI file format;

[0021] Removing motion artifacts from the NIfTI file to obtain an image with motion artifacts removed;

[0022] stripping the skull region from the motion-free image using a digital algorithm;

[0023] The brain image obtained after the skull is peeled off is registered to the template of the standard space.

[0024] From the above, by converting the format to NIfTI, the file is more versatile and can be used for other subsequent processing; removing motion artifacts reduces system errors caused by subject movement and machine instability; by stripping away the influence of the skull, the brain's organizational structure is better displayed and accuracy is improved; aligning the image to a standard space template helps to accurately locate spatial information and facilitate subsequent multimodal data integration and analysis.

[0025] As a possible implementation manner of the first aspect, removing motion artifacts refers to removing the images of the first five time points in the NIfTI file, and selecting the image of the first time point after the removal.

[0026] From the above, due to the instability of the machine at the beginning of the scan and the possibility that the subject has a large head movement, the data of the first 5 time points are removed and the image of the first time point after the data is removed is selected, so the result is more accurate.

[0027] As a possible implementation of the first aspect, the acquisition of magnetic resonance brain imaging signals is to acquire blood oxygen level-dependent functional imaging; the prior template is an EPI template in the MNI space, and a mask corresponding to the EPI template is used for processing.

[0028] From the above, for blood oxygen level dependent functional imaging, using the corresponding MNI space EPI template and its mask for processing can improve the spatial resolution and enhance the processing speed and quality.

[0029] The second aspect of the present application provides a detection device for magnetic resonance brain imaging signal loss, comprising:

[0030] The data acquisition and preprocessing module is used to acquire magnetic resonance brain imaging signals and perform preprocessing to obtain brain imaging images;

[0031] A feature extraction module, which extracts the image features of the brain image and the prior template respectively in the same way;

[0032] The signal missing judgment module is used to compare the image features with the image features of the prior template to determine whether the magnetic resonance brain imaging signal is missing.

[0033] A third aspect of the present application provides a computing device, comprising: a processor, and a memory, on which program instructions are stored, and when the program instructions are executed by the processor, the processor executes the method for detecting the absence of magnetic resonance brain imaging signals as described in any one of the first aspects.

[0034] A fourth aspect of the present application provides a computer-readable storage medium having program instructions stored thereon, wherein when the program instructions are executed by a computer, the computer executes the method for detecting the absence of magnetic resonance brain imaging signals as described in any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of a first embodiment of a method for detecting missing magnetic resonance brain imaging signals of the present application;

[0036] Figure 2 is a flow chart of a second embodiment of the method for detecting missing magnetic resonance brain imaging signals of the present application;

[0037] Figure 3 is a schematic diagram of signal loss involved in the second embodiment of the present application;

[0038] Figure 4 is a schematic diagram of maximum density projections in sagittal, coronal and transverse positions of the a priori template involved in the second embodiment of the present application;

[0039] Figure 5 is a schematic diagram of a detection device for magnetic resonance brain imaging signal loss provided in an embodiment of the present application;

[0040] Figure 6 It is a structural schematic diagram of a computing device provided in an embodiment of the present application.

[0041] It should be understood that the size and shape of each block diagram in the above structural diagram are for reference only and should not constitute an exclusive interpretation of the embodiment of the present invention. The relative position and inclusion relationship between the blocks presented in the structural diagram are only schematic representations of the structural association between the blocks, and do not limit the physical connection method of the embodiment of the present invention. DETAILED DESCRIPTION

[0042] The technical solution provided by the present application is further described below with reference to the accompanying drawings and examples. It should be understood that the system structure and business scenarios provided in the examples of the present application are mainly to illustrate the possible implementation methods of the technical solution of the present application and should not be interpreted as the only limitation on the technical solution of the present application. It is known to those of ordinary skill in the art that with the evolution of the system structure and the emergence of new business scenarios, the technical solution provided by the present application is also applicable to similar technical problems.

[0043] It should be understood that the detection scheme for the absence of magnetic resonance brain imaging signals provided in the embodiments of the present application includes a detection method, device, computing device, and readable storage medium for the absence of magnetic resonance brain imaging signals. Since the principles of solving the problems in these technical schemes are the same or similar, some repetitions may not be repeated in the introduction of the following specific embodiments, but it should be regarded that these specific embodiments have been referenced to each other and can be combined with each other.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application. In the event of any inconsistency, the meaning described in this specification or the meaning derived from the contents recorded in this specification shall prevail. In addition, the terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application. In order to accurately describe the technical content in this application and to accurately understand the present invention, the following explanations or definitions are given to the terms used in this specification before describing the specific embodiments:

[0045] 1) Blood Oxygen Level Dependent Functional Magnetic Resonance Imaging (BOLD fMRI): It is a functional magnetic resonance imaging technology that reflects changes in neural activity by detecting changes in blood oxygen levels in the brain.

[0046] 2) Digital Imaging and Communications in Medicine (DICOM): defines a special file format for storing medical images and related metadata. This format contains not only the image itself, but also detailed metadata such as the patient's personal information, equipment parameters, and imaging conditions.

[0047] 3) Neuroimaging Informatics Technology Initiative (NIfTI): NIfTI is a widely used file format for storing and processing medical imaging data. NIfTI is based on the earlier ANALYZE 7.5 format but adds more functionality and flexibility.

[0048] 4) Maximum Intensity Projection (MIP): It is a technique for obtaining two-dimensional images through perspective. It is obtained by calculating the maximum density pixels encountered along each ray of the scanned object. It is a technique widely used in the field of medical imaging.

[0049] 5) Montreal Neurological Institute StandardSpace (MNI): It is a standardized brain coordinate system developed by the Montreal Neurological Institute in Canada and is widely used in neuroimaging research.

[0050] 6) Echo Planar Imaging (EPI): This is a standardized template commonly used in functional magnetic resonance imaging (fMRI), especially suitable for images generated by EPI sequences. EPI sequences are widely used in fMRI research due to their rapid imaging characteristics, and can capture the functional activities of the brain when performing specific tasks or in a resting state.

[0051] The detection scheme for missing magnetic resonance brain imaging signals provided in the embodiment of the present application compares the image features of the preprocessed image with the image features of the prior template, reduces the sensitivity of the input image quality, occupies less system computing resources, and provides higher accuracy. It determines whether the magnetic resonance brain imaging signal is missing through standard quantitative indicators, thereby improving the level of automation. The embodiment of the present application can be applied to the detection of missing magnetic resonance brain imaging signals in magnetic resonance brain imaging scanning technology in various medical, scientific research and other fields. The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0052] The first embodiment of the present application provides a method for detecting the absence of magnetic resonance brain imaging signals. Figure 1 , the implementation of each step of the method is specifically described, including steps S10-S30.

[0053] S10: acquiring magnetic resonance brain imaging signals and performing preprocessing to obtain brain imaging images;

[0054] In some embodiments, magnetic resonance brain imaging signals are mainly acquired using a magnetic resonance imager to acquire BOLD fMRI, sMRI and other magnetic resonance imaging data; wherein, auxiliary equipment such as patient positioning, respiratory gating, ECG gating, noise control, temperature control, etc. may also be used to assist in scanning; imaging parameters may also be adjusted using T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging and other technologies to acquire different sequence data to meet different clinical diagnosis, scientific research and other needs.

[0055] In some embodiments, when acquiring magnetic resonance brain imaging signals, DICOM, NIfTI, Analyze, MINC, NRRD and other file formats are directly output for subsequent research according to different instruments and software; file conversion can also be performed according to the output file format to meet different processing requirements, such as converting the DICOM file format into the NIfTI file format.

[0056] In some embodiments, preprocessing may include denoising, artifact removal, field strength non-uniformity correction, image registration, image segmentation, image reconstruction, standardization (normalization), skull stripping, etc.; wherein, skull stripping can be achieved by using the Bet command in the image analysis tool FSL, or by statistical calculation methods such as deep learning, machine learning, etc. such as 3D U-Net; wherein, the patient's motion artifacts can be removed manually by removing interference, such as using the fslroi tool in FSL to remove the data of the first 5 time points, and then selecting the image of the first time point after removal; and gated assisted monitoring and other methods can also be used to reduce motion interference; wherein, standardization (normalization) aligns brain image data to standard reference spaces such as MNI, Talairach, ICBM and other standard spaces; MNI standard space provides a variety of different templates such as MNI305, Colin27, EPI, etc., and magnetic resonance brain images can be aligned to different templates according to different processing requirements, such as functional magnetic resonance imaging images can be aligned to the EPI template of the MNI space.

[0057] S20: extracting image features of the brain image and the priori template respectively in the same manner;

[0058] In some embodiments, extracting image features of a brain image and a priori templates can extract image features such as statistical features, shape features, texture features, signal intensity changes, time series changes, and pixel grayscale values; wherein shape features may include area, perimeter, volume, number of voxels, etc.; wherein area may include sagittal, coronal, and transverse areas, etc.; calculation of sagittal, coronal, and transverse areas can be performed according to different needs and scenarios by maximum density projection, central slice method, average projection method, minimum density projection method, median projection method, etc. to obtain sagittal, coronal, and transverse images, and obtain the images by pixel counting or using a specific image analysis algorithm.

[0059] In some embodiments, image features may also be analyzed and extracted through statistical analysis, deep learning and other algorithms.

[0060] In some embodiments, the prior template refers to a standard template image with no signal loss, such as the EPI template of the MNI standard reference space, which can be processed by the "BrainMask_05_91x109x91" mask provided by DPABI; the method, steps and content of image feature extraction should be consistent with the method, steps and content of brain imaging.

[0061] S30: Compare the brain image with the image features of the prior template to determine whether the magnetic resonance brain image signal is missing.

[0062] In some embodiments, comparing the image features of the brain image with those of the prior template to determine whether the magnetic resonance brain image signal is missing is performed by determining whether the image information is complete; for example, the ratio of the number of voxels, sagittal, coronal, and transverse areas of the brain image to the number of voxels, sagittal, coronal, and transverse areas of the prior template in the standard space can be calculated and multiplied by 100% to obtain the percentage of the information of the acquired brain image to the information that should be acquired under the complete standard state, and by setting a threshold or combining experience to evaluate whether the percentage meets the standard, it can be determined whether the magnetic resonance brain image signal is missing.

[0063] In some embodiments, the evaluation results can be saved as a file to facilitate subsequent adjustments to the scanning plan based on the judgment results; the adjustment methods include adjusting scanning parameters, subject status, hardware equipment inspection and maintenance, improving preprocessing steps, etc.

[0064] The second embodiment of the present application provides a method for detecting the absence of magnetic resonance brain image signals for blood oxygen level dependent functional imaging. Figure 2 The method provided by the second embodiment includes the following steps S200-S250.

[0065] S200: Scan and obtain a DICOM format file of a magnetic resonance brain image, and convert the DICOM format file into a NIfTI format file.

[0066] First, the subjects were scanned by magnetic resonance imaging to collect data and obtain DICOM format files for blood level dependent functional imaging data; then the dcm2nii tool was used to convert the obtained DICOM format files into NIfTI format files. Figure 3 shown.

[0067] S210: After removing the first five time point images, select the first time point image, remove the skull from the image, and register it to the EPI template in the MNI space.

[0068] Considering that the machine is unstable at the beginning of scanning and the subjects may have large head movements, the data of the first five time points were removed, and then the image of the first time point (corresponding to the original sixth time point) was selected as the magnetic resonance brain image to be detected.

[0069] The skull was peeled off using the Bet command in the image analysis tool FSL to remove the skull image and better display the brain's tissue structure.

[0070] The brain images after removing behavioral influences and skull stripping were registered to the EPI template in MNI space.

[0071] S220: Calculate the number of voxels of the registered image, as well as the sagittal, coronal, and transverse areas.

[0072] The code was used to sum the number of voxels in the registered images; the maximum intensity projection method was used to calculate the sagittal, coronal, and transverse areas of the registered images.

[0073] S230: Calculate the number and area of ​​voxels of the prior template using the same method as that used to calculate the magnetic resonance brain image.

[0074] The number and area of ​​voxels in the EPI template are calculated using the mask "BrainMask_05_91x109x91" in the magnetic resonance imaging analysis tool DPABI. The sagittal, coronal, and transverse views of the a priori template obtained using the maximum density projection method are shown in the figure below. Figure 4 shown.

[0075] S240: Calculate the percentage of the image and area in the prior template; output relevant results, and determine whether the magnetic resonance brain imaging signal is missing.

[0076] Calculate the percentage of the number of voxels, sagittal, coronal, and transverse areas of the registered image to the number of voxels, sagittal, coronal, and transverse areas of the prior template (ie, EPI template), and output the results.

[0077] Scanners and scientific researchers can evaluate image quality based on the results and adjust scanning plans in a timely manner.

[0078] The third embodiment of the present application provides a detection device for the absence of magnetic resonance brain imaging signals, which can be used to implement the application compression method in the above embodiment, such as Figure 5 As shown, the detection device for missing magnetic resonance brain imaging signals includes:

[0079] The data acquisition and preprocessing module is used to acquire magnetic resonance brain imaging signals and perform preprocessing to obtain brain imaging images; specifically, the data acquisition and preprocessing module can be used to implement step S10 in the first embodiment and its optional embodiments.

[0080] The feature extraction module extracts the image features of the brain image and the prior template in the same way; specifically, the data acquisition and preprocessing module can be used to implement step S20 in the first embodiment and its optional embodiments.

[0081] The signal missing judgment module is used to compare the image features of the image with the image features of the prior template to determine whether the magnetic resonance brain imaging signal is missing; specifically, the data acquisition and preprocessing module can be used to implement step S30 in the first embodiment and its optional embodiments.

[0082] Figure 6 900 is a schematic structural diagram of a computing device 900 provided in an embodiment of the present application. The computing device can execute each optional embodiment of the above method, and the computing device can be a terminal, or a chip or chip system inside the terminal. Figure 6 As shown, the computing device 900 includes: a processor 910 , a memory 920 , and a communication interface 930 .

[0083] It should be understood that Figure 6 The communication interface 930 in the computing device 900 shown may be used to communicate with other devices, and may specifically include one or more transceiver circuits or interface circuits.

[0084] The processor 910 may be connected to a memory 920. The memory 920 may be used to store the program code and data. Therefore, the memory 920 may be a storage unit inside the processor 910, or an external storage unit independent of the processor 910, or a component including a storage unit inside the processor 910 and an external storage unit independent of the processor 910.

[0085] Optionally, the computing device 900 may further include a bus. The memory 920 and the communication interface 930 may be connected to the processor 910 via the bus. The bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 A line without an arrow is used to represent the bus, but this does not mean that there is only one bus or one type of bus.

[0086] It should be understood that in the embodiment of the present application, the processor 910 may adopt a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. Alternatively, the processor 910 may adopt one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiment of the present application.

[0087] The memory 920 may include a read-only memory and a random access memory, and provides instructions and data to the processor 910. A portion of the processor 910 may also include a nonvolatile random access memory. For example, the processor 910 may also store information on the device type.

[0088] When the computing device 900 is running, the processor 910 executes the computer-executable instructions in the memory 920 to perform any operation step of the above method and any optional embodiment thereof.

[0089] It should be understood that the computing device 900 according to the embodiment of the present application can correspond to the corresponding subjects in the methods according to the embodiments of the present application, and the above-mentioned and other operations and / or functions of each module in the computing device 900 are respectively for implementing the corresponding processes of each method of the present embodiment. For the sake of brevity, they will not be repeated here.

[0090] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0095] If the functions 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. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0096] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it is used to execute the above method, which includes at least one of the solutions described in the above embodiments.

[0097] The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive lists) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing programs, which can be used by instruction execution systems, devices or devices or used in combination with them.

[0098] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0099] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0100] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0101] In addition, the words "first, second, third, etc." or module A, module B, module C and other similar terms in the specification and claims are only used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that the specific order or sequence can be interchanged where permitted so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0102] In the above description, the numbers representing the steps, such as S110, S120, etc., do not necessarily mean that the steps will be executed in this manner. If permitted, the order of the steps can be interchanged or they can be executed simultaneously.

[0103] The term "comprising" as used in the description and claims should not be interpreted as being limited to what is listed thereafter; it does not exclude other elements or steps. Therefore, it should be interpreted as specifying the presence of the features, integers, steps or components mentioned, but does not exclude the presence or addition of one or more other features, integers, steps or components and groups thereof. Therefore, the expression "a device comprising means A and B" should not be limited to a device consisting of components A and B only.

[0104] References to "one embodiment" or "an embodiment" in this specification mean that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present application. Therefore, the phrases "in one embodiment" or "in an embodiment" appearing in various places in this specification do not necessarily refer to the same embodiment, but may refer to the same embodiment. In addition, in one or more embodiments, the particular features, structures, or characteristics can be combined in any appropriate manner, as would be apparent to one of ordinary skill in the art from this disclosure.

[0105] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may also include more other equivalent embodiments without departing from the concept of the present application, all of which belong to the scope of protection of the present application.

Claims

1. A method for detecting missing magnetic resonance brain imaging signals, characterized in that: include: Acquire magnetic resonance brain imaging signals and perform preprocessing to obtain brain imaging images; Extracting the image features of the brain image and the prior template respectively in the same manner; The brain image is compared with the image features of the prior template to determine whether the magnetic resonance brain image signal is missing.

2. The method according to claim 1, characterized in that The extraction of image features includes calculating the number of voxels, sagittal, coronal and transverse areas.

3. The method according to claim 2, characterized in that The sagittal, coronal and transverse areas are calculated by maximum intensity projection method.

4. The method according to claim 2, characterized in that: The image features of the brain image are compared with those of the prior template by calculating the number of voxels, sagittal, coronal and transverse areas of the brain image as a percentage of the prior template; the determination of whether the magnetic resonance brain image signal is missing is evaluated based on the size of the percentage, and the scanning plan is adjusted based on the evaluation result.

5. The method according to claim 1, characterized in that The pre-processing comprises: Converting the acquired magnetic resonance brain image signal in a non-NIfTI file format into a NIfTI file format; Removing motion artifacts from the NIfTI file to obtain an image with motion artifacts removed; stripping the skull region from the motion-free image using a digital algorithm; The brain image obtained after the skull is peeled off is registered to the template of the standard space.

6. The method according to claim 5, characterized in that The removal of motion artifacts refers to removing the images of the first five time points in the NIfTI file, and selecting the first time point image after the removal.

7. The method according to claim 5, characterized in that The acquisition of magnetic resonance brain imaging signals is to obtain functional imaging based on blood oxygen level dependence; the prior template is an EPI template in the MNI space, and the mask corresponding to the EPI template is used for processing.

8. A device for detecting missing magnetic resonance brain image signals, characterized in that: include: The data acquisition and preprocessing module is used to acquire magnetic resonance brain imaging signals and perform preprocessing to obtain brain imaging images; A feature extraction module, which extracts the image features of the brain image and the prior template respectively in the same way; The signal missing judgment module is used to compare the image features with the image features of the prior template to determine whether the magnetic resonance brain imaging signal is missing.

9. A computing device, characterized in that include: processor, and A memory having program instructions stored thereon, wherein when the program instructions are executed by the processor, the processor executes the method for detecting the absence of magnetic resonance brain imaging signals as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Program instructions are stored thereon, and when the program instructions are executed by a computer, the computer is caused to execute the method for detecting the absence of magnetic resonance brain imaging signals as claimed in any one of claims 1 to 7.

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