A resting state imaging and functional connectivity analysis method and apparatus

Abstract

The application provides a resting state imaging and functional connection analysis method and device, the method comprises the following steps: collecting k-space data of brain resting state, the magnetic resonance echo planar imaging sequence comprises a plurality of k-space phase encoding direction transformation periods, the k-space phase encoding direction corresponding to a plurality of repetition times in each k-space phase encoding direction transformation period is different from each other, and the k-space phase encoding direction corresponding to any one repetition time exists the k-space phase encoding direction corresponding to another repetition time which is opposite in direction; grouping the functional magnetic resonance image to obtain a plurality of image sets; calculating the brain functional connection of each image set to obtain the functional connection result corresponding to each image set; and calculating the average value of the functional connection result corresponding to each image set to obtain the final brain resting state functional connection result. The resting state imaging and functional connection analysis method and device provided by the embodiment of the application improve the reliability of the brain resting state functional connection result.

Description

Technical Field

[0001] This invention relates to the fields of magnetic resonance imaging and data processing technology, and specifically to a method and apparatus for resting-state imaging and functional connectivity analysis. Background Technology

[0002] Currently, resting-state functional magnetic resonance imaging (rs-fMRI) is an important neuroimaging technique used to study the functional connectivity patterns of the brain in a resting state.

[0003] In existing technologies, the traditional imaging sequence for rs-fMRI is the echo planar imaging (EPI) sequence. However, EPI sequences are susceptible to interference from factors such as motion artifacts, physiological noise (e.g., heartbeat and respiration), and magnetic field inhomogeneities, which significantly reduce the reliability of functional connectivity. Magnetic field inhomogeneities can lead to EPI image distortion and signal loss, especially at high field strengths (e.g., 3T or 7T), where this distortion problem is more severe. Image distortion significantly affects the reliability of functional connectivity. Therefore, how to propose a resting-state functional connectivity analysis method to improve the reliability of resting-state functional connectivity results has become an important issue that urgently needs to be addressed in this field. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a resting-state imaging and functional connectivity analysis method and apparatus, which can at least partially solve the problems existing in the prior art.

[0005] In a first aspect, the present invention proposes a method for resting-state imaging and functional connectivity analysis, comprising:

[0006] The resting-state k-space data of the brain is acquired using a magnetic resonance echo-planar imaging sequence. This sequence includes multiple k-space phase encoding direction transformation cycles, each cycle comprising multiple repetition times. The k-space phase encoding directions corresponding to the multiple repetition times within each cycle are distinct, and for any repetition time, there exists an opposite k-space phase encoding direction corresponding to another repetition time.

[0007] Image reconstruction was performed on the k-space data of the resting state of the brain, and the resulting functional magnetic resonance images were grouped to obtain multiple image sets;

[0008] Calculate the brain functional connectivity for each image set to obtain the functional connectivity results for each image set;

[0009] The average value of the functional connectivity results corresponding to each image set was calculated to obtain the final resting-state functional connectivity results of the brain.

[0010] Furthermore, the process of reconstructing images from the k-space data of the resting state of the brain, and grouping the resulting functional magnetic resonance images to obtain multiple image sets includes:

[0011] Functional magnetic resonance images obtained by image reconstruction based on the k-space data of the resting state of the brain are grouped according to the k-space phase encoding direction to obtain multiple image sets. The number of image sets is equal to the number of k-space phase encoding directions within the k-space phase encoding direction transformation cycle. The number of three-dimensional brain images included in each image set is equal to the number of k-space phase encoding direction transformation cycles included in the magnetic resonance echo planar imaging sequence.

[0012] Furthermore, the k-space phase encoding direction corresponding to each repetition time within each cycle rotates sequentially in either a counterclockwise or clockwise direction.

[0013] Furthermore, the number of repetition times within a single k-space phase encoding direction transformation cycle is less than a preset value.

[0014] Furthermore, the duration of the k-space phase encoding direction transformation period is less than 5 seconds.

[0015] Secondly, the present invention proposes a resting-state functional connectivity analysis device, comprising:

[0016] The acquisition unit is used to acquire k-space data of the brain in a resting state. The k-space data of the brain in a resting state is acquired through a magnetic resonance echo-planar imaging sequence. The magnetic resonance echo-planar imaging sequence includes multiple k-space phase coding direction transformation cycles. Each k-space phase coding direction transformation cycle includes multiple repetition times. The k-space phase coding directions corresponding to the multiple repetition times within each k-space phase coding direction transformation cycle are different from each other, and for any k-space phase coding direction corresponding to any repetition time, there is another k-space phase coding direction corresponding to a repetition time with the opposite direction.

[0017] The grouping unit is used to perform image reconstruction on the k-space data of the resting state of the brain, and to group the generated functional magnetic resonance images to obtain multiple image sets.

[0018] The computing unit is used to calculate the brain functional connectivity of each image set and obtain the functional connectivity results corresponding to each image set.

[0019] The acquisition unit is used to calculate the average value of the functional connectivity results corresponding to each set of images to obtain the final resting-state functional connectivity results of the brain.

[0020] Furthermore, the grouping unit is specifically used for:

[0021] The functional magnetic resonance images obtained by image reconstruction based on the k-space data of the resting state of the brain are grouped according to the k-space phase encoding direction to obtain multiple image sets. The number of image sets is equal to the number of k-space phase encoding directions within the k-space phase encoding direction transformation cycle. The number of three-dimensional brain images included in each image set is equal to the number of k-space phase encoding direction transformation cycles included in the magnetic resonance echo planar imaging sequence. Furthermore, the k-space phase encoding direction corresponding to each repetition time within each k-space phase encoding direction transformation cycle is rotated sequentially in a counterclockwise or clockwise direction.

[0022] Furthermore, the number of repetition times within a single k-space phase encoding direction transformation cycle is less than a preset value.

[0023] Furthermore, the duration of the k-space phase encoding direction transformation period is less than 5 seconds.

[0024] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the resting-state imaging and functional connectivity analysis method described in any of the above embodiments.

[0025] Fourthly, the present invention provides a computer-readable storage medium storing a computer program / instructions that, when executed by a processor, implement the resting-state imaging and functional connectivity analysis method described in any of the above embodiments.

[0026] Fifthly, the present invention provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the resting-state imaging and functional connectivity analysis method described in any of the above embodiments.

[0027] The resting-state imaging and functional connectivity analysis method and apparatus provided in this invention can acquire resting-state k-space data of the brain. This resting-state k-space data is acquired through a magnetic resonance echo-planar imaging sequence. The magnetic resonance echo-planar imaging sequence includes multiple k-space phase coding direction transformation cycles, each k-space phase coding direction transformation cycle includes multiple repetition times. The k-space phase coding directions corresponding to the multiple repetition times within each k-space phase coding direction transformation cycle are all different, and for any repetition time, there exists another repetition time with an opposite k-space phase coding direction. Image reconstruction is performed on the resting-state k-space data, and the resulting functional magnetic resonance images are grouped to obtain multiple image sets. Brain functional connectivity is calculated for each image set to obtain the functional connectivity results corresponding to each image set. The average value of the functional connectivity results for each image set is calculated to obtain the final resting-state functional connectivity results. This method can acquire image sets with different k-space phase coding directions based on changes in the k-space phase coding direction. By calculating the average value of functional connectivity, interference caused by a single phase coding direction can be eliminated, improving the reliability of the resting-state functional connectivity results. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0029] Figure 1 This is a flowchart illustrating the resting-state imaging and functional connectivity analysis method provided in the first embodiment of the present invention.

[0030] Figure 2 This is a schematic diagram of the k-space phase encoding direction switching corresponding to the TR of a single k-space phase encoding direction transformation period provided in the second embodiment of the present invention.

[0031] Figure 3 This is a schematic diagram of the k-space phase encoding direction switching corresponding to the TR of a single k-space phase encoding direction transformation period provided in the third embodiment of the present invention.

[0032] Figure 4 This is a flowchart illustrating the resting-state imaging and functional connectivity analysis method provided in the fourth embodiment of the present invention.

[0033] Figure 5 This is a schematic diagram of the EPI image acquired by switching the EPI phase encoding direction up and down according to the fifth embodiment of the present invention.

[0034] Figure 6 This is a schematic diagram illustrating how the resting-state functional connection reliability changes with increasing scan time, as provided in the sixth embodiment of the present invention.

[0035] Figure 7 This is a schematic diagram of the structure of the resting-state imaging and functional connectivity analysis device provided in the seventh embodiment of the present invention.

[0036] Figure 8 This is a schematic diagram of the physical structure of the computer device provided in the eighth embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0038] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0039] To facilitate understanding of the technical solution provided in this application, the relevant content of the technical solution in this application will be explained below.

[0040] Reliability and reproducibility of functional connectivity are important indicators for assessing the quality of rs-fMRI data and the credibility of research results. Reliability reflects the consistency of functional connectivity measurements across different scan time points or different subjects. High reliability is particularly important for resting-state imaging, as its research goals are usually to reveal stable brain functional network features rather than transient dynamic changes. Insufficient reliability may lead to biased or unreproducible results, thereby affecting the scientific understanding of brain function and the accuracy of clinical applications.

[0041] EPI sequences are susceptible to interference from motion artifacts, physiological noise, and magnetic field inhomogeneities, which significantly reduce the reliability of functional connectivity. To address the issues of motion artifacts and physiological noise, multi-echo (ME) EPI sequences have been proposed. ME imaging acquires signals at multiple echo times (TE) and utilizes signal attenuation characteristics to distinguish between BOLD (Blood Oxygenation Level Dependent) signals and non-BOLD noise, thereby effectively removing the effects of motion artifacts and physiological noise and improving the signal-to-noise ratio and reliability of rs-fMRI data.

[0042] However, the multi-echo EPI method also has certain limitations. First, to acquire multiple echo signals, the imaging time is usually extended, limiting the temporal resolution. Second, multi-echo imaging places high demands on hardware performance, leading to a reduction in spatial resolution. Third, it cannot address interference from factors such as magnetic field inhomogeneity. Magnetic field inhomogeneity can cause EPI image distortion and signal loss, especially at high field strengths (such as 3T or 7T), where this distortion problem is more severe. Image distortion significantly affects the reliability of functional connections. The following are the specific impacts of EPI image distortion on the reliability of functional connections:

[0043] (1) Spatial localization error: EPI image distortion can cause spatial localization shifts in brain anatomy, especially in areas with high magnetic field inhomogeneity (such as the frontal and temporal lobes near the sinuses). This spatial localization error can lead to misattribution of functional signals, resulting in inaccurate measurements of functional connectivity and thus reduced reliability. For example, the degree of distortion may vary between different scanning time points or between different subjects, leading to a lack of consistency in the measurements of functional connectivity.

[0044] (2) Signal loss and artifacts: In regions with strong magnetic field gradients (such as those near the skull base), EPI sequences are prone to signal loss or artifacts. These signal losses can prevent the accurate capture of functional signals in certain brain regions, thus affecting the integrity and reliability of functional connectivity. In addition, artifacts may introduce false signal changes, further interfering with the measurement of functional connectivity.

[0045] (3) Reduced repeatability across time points: Distortion issues may exhibit different patterns at different scanning time points, especially when the subject's head position changes slightly. This variation can lead to inconsistent functional connectivity measurements across time points, thereby reducing reliability.

[0046] (4) Impact on subsequent analysis: EPI image distortion can also affect subsequent preprocessing steps (such as registration and normalization). Due to spatial errors caused by distortion, there is a risk of inaccurate registration between functional and anatomical images, which in turn affects the calculation results of functional connectivity. This error will be further amplified in population analysis, reducing the reliability of the research results.

[0047] Therefore, embodiments of the present invention provide a resting-state imaging and functional connectivity analysis method, which improves the reliability of functional connectivity by reducing the interference of magnetic field inhomogeneity on single-phase-encoded direction EPI images, while avoiding the problems of increased imaging time and decreased image resolution caused by multi-echo methods in the prior art.

[0048] Figure 1 This is a flowchart illustrating the resting-state imaging and functional connectivity analysis method provided in the first embodiment of the present invention, as shown below. Figure 1 As shown, the resting-state imaging and functional connectivity analysis method provided in this embodiment of the invention includes:

[0049] S101. Acquire k-space data of the brain in resting state, wherein the k-space data of the brain in resting state is acquired by magnetic resonance echo-planar imaging sequence; wherein the magnetic resonance echo-planar imaging sequence includes multiple k-space phase coding direction transformation cycles, each k-space phase coding direction transformation cycle includes multiple repetition times, the k-space phase coding directions corresponding to the multiple repetition times within each k-space phase coding direction transformation cycle are all different, and for any repetition time corresponding to the k-space phase coding direction, there is another repetition time corresponding to the opposite k-space phase coding direction;

[0050] Specifically, resting-state k-space data of the brain can be acquired using a magnetic resonance echo-planar imaging sequence. The magnetic resonance echo-planar imaging sequence includes multiple k-space phase-encoding direction transformation cycles, each cycle comprising multiple repetition times (TRs). Each TR acquires one or more layers of magnetic resonance images. The k-space phase-encoding directions corresponding to each TR within each k-space phase-encoding direction transformation cycle are distinct. Furthermore, within each k-space phase-encoding direction transformation cycle, for any repetition time, there exists a k-space phase-encoding direction with the opposite direction corresponding to another repetition time. The number of TRs within each k-space phase-encoding direction transformation cycle is set according to actual needs, and this embodiment of the invention does not limit this.

[0051] The magnetic resonance echo-plane imaging sequence includes, but is not limited to, gradient echo-plane sequences, spin echo-plane sequences, and stimulated echo-plane sequences. The sequence type of the magnetic resonance echo-plane imaging sequence is selected according to actual needs, and this embodiment of the invention does not impose any limitations. Furthermore, the length of the echo train and the number of echoes it contains are set according to experimental or clinical needs, and this embodiment of the invention does not impose any limitations.

[0052] For example, such as Figure 2 As shown, each k-space phase encoding direction transformation cycle includes four TRs, and the k-space phase encoding directions corresponding to the four TRs switch every 90° in a counterclockwise direction. Within a single k-space phase encoding direction transformation cycle, the k-space phase encoding direction corresponding to the first TR is opposite to the k-space phase encoding direction corresponding to the third TR; the k-space phase encoding direction corresponding to the second TR is opposite to the k-space phase encoding direction corresponding to the fourth TR.

[0053] For example, each k-space phase encoding direction transformation period includes two TRs, and the k-space phase encoding directions corresponding to the two TRs switch every 180° in a counterclockwise direction, such as... Figure 3 As shown, within a single k-space phase coding direction transformation period, the k-space phase coding directions corresponding to the two TRs are opposite.

[0054] By designing dynamic changes in the k-space phase coding direction corresponding to each repetition time within each k-space phase coding direction transformation cycle, the impact of image distortion in a single phase coding direction on resting-state reliability can be effectively reduced.

[0055] S102. Perform image reconstruction on the k-space data of the resting state of the brain, and group the generated functional magnetic resonance images to obtain multiple image sets;

[0056] Specifically, by reconstructing the k-space data of the resting state of the brain acquired by the magnetic resonance echo planar imaging sequence, functional magnetic resonance images can be obtained. The obtained functional magnetic resonance images can be grouped according to the k-space phase encoding direction to obtain multiple image sets.

[0057] S103. Calculate the brain functional connectivity for each image set and obtain the functional connectivity results corresponding to each image set.

[0058] Specifically, after obtaining the aforementioned multiple image sets, brain functional connectivity is calculated for each image set to obtain the functional connectivity results corresponding to each image set. The calculation of brain functional connectivity can employ methods such as correlation analysis, graph theory analysis, and machine learning analysis, selected according to actual needs; this embodiment of the invention does not impose any limitations.

[0059] S104. Calculate the average value of the functional connectivity results corresponding to each set of images to obtain the final resting-state functional connectivity results of the brain.

[0060] Specifically, after obtaining the functional connectivity results corresponding to each set of images, the average value of the functional connectivity results for each set of images is calculated to obtain the final resting-state functional connectivity results of the brain. By averaging the functional connectivity results corresponding to each set of images, interference caused by a single phase encoding direction can be eliminated, thereby obtaining more reliable resting-state functional connectivity results of the brain.

[0061] The resting-state imaging and functional connectivity analysis method provided in this invention collects resting-state k-space data of the brain. This k-space data is acquired through a magnetic resonance echo-planar imaging sequence, which includes multiple k-space phase coding direction transformation cycles. Each k-space phase coding direction transformation cycle includes multiple repetition times. The k-space phase coding directions corresponding to the multiple repetition times within each k-space phase coding direction transformation cycle are different, and for any repetition time, there exists another repetition time with an opposite k-space phase coding direction. Image reconstruction is performed on the resting-state k-space data, and the resulting functional magnetic resonance images are grouped to obtain multiple image sets. Brain functional connectivity is calculated for each image set to obtain the functional connectivity results corresponding to each image set. The average value of the functional connectivity results for each image set is calculated to obtain the final resting-state functional connectivity results. This method can acquire image sets with different k-space phase coding directions based on changes in the k-space phase coding direction. By calculating the average value of functional connectivity, interference caused by a single phase coding direction can be eliminated, improving the reliability of the resting-state functional connectivity results.

[0062] Based on the above embodiments, further, the step of performing image reconstruction on the k-space data of the resting state of the brain, and grouping the resulting functional magnetic resonance images to obtain multiple image sets includes:

[0063] Functional magnetic resonance images obtained by image reconstruction based on the k-space data of the resting state of the brain are grouped according to the k-space phase encoding direction to obtain multiple image sets. The number of image sets is equal to the number of k-space phase encoding directions within the k-space phase encoding direction transformation cycle. The number of three-dimensional brain images (volumes) included in each image set is equal to the number of k-space phase encoding direction transformation cycles included in the magnetic resonance echo planar imaging sequence.

[0064] Specifically, the functional magnetic resonance imaging (fMRI) images obtained by image reconstruction based on the k-space data of the resting state of the brain are grouped according to the k-space phase encoding direction. FMRI images corresponding to the same k-space phase encoding direction are grouped together, resulting in multiple image sets. The number of image sets is equal to the number of k-space phase encoding directions within a single k-space phase encoding direction transformation cycle, and the number of three-dimensional brain images included in each image set is equal to the number of k-space phase encoding direction transformation cycles included in the magnetic resonance echo-planar imaging sequence.

[0065] For example, such as Figure 2 As shown, the number of k-space phase coding directions corresponding to each repetition time within a single k-space phase coding direction transformation period is 4, and the number of image sets is equal to 4; as Figure 3 As shown, the number of k-space phase coding directions corresponding to each repetition time within a single k-space phase coding direction transformation cycle is 2, and the number of image sets is equal to 2.

[0066] Based on the above embodiments, the k-space phase encoding direction corresponding to each repetition time within each k-space phase encoding direction transformation cycle is rotated sequentially in either a counterclockwise or clockwise direction.

[0067] For example, each k-space phase coding direction transformation cycle includes 4 TRs, the k-space phase coding directions corresponding to the 4 TRs rotate sequentially in a clockwise direction, and the rotation angle between adjacent TRs corresponding to the k-space phase coding directions in a clockwise direction is 90 degrees.

[0068] For example, there are 8 TRs in each k-space phase coding direction transformation cycle. The k-space phase coding directions corresponding to the 8 TRs rotate sequentially in a clockwise direction, and the rotation angle between adjacent TRs in the clockwise direction is 45 degrees.

[0069] By using this design of dynamically switching phase encoding directions, the impact of image distortion caused by a single phase encoding direction on resting-state functional connectivity can be effectively reduced, thus providing a more reliable data foundation for subsequent functional connectivity analysis.

[0070] Based on the above embodiments, the number of repetition times within a single k-space phase encoding direction transformation cycle is further less than a preset value.

[0071] Specifically, in rs-fMRI, it is generally desirable to have the shortest possible repeat time (TR) to improve temporal resolution and thus better capture rapid fluctuations in brain activity. Shorter TRs risk reducing the signal-to-noise ratio (SNR) and also increase the hardware burden. Since the k-space phase encoding direction corresponding to each TR changes continuously within a single k-space phase encoding direction transformation cycle, to meet the temporal resolution requirements, the number of TRs within a single k-space phase encoding direction transformation cycle cannot be too large, and the number of repetition times within a single k-space phase encoding direction transformation cycle must be less than a preset value to meet the temporal resolution requirements of magnetic resonance imaging. The preset value is set according to actual needs, and this embodiment of the invention does not impose any limitations.

[0072] Based on the above embodiments, the duration of the k-space phase encoding direction transformation period is further less than 5 seconds.

[0073] Specifically, to ensure that the data of each k-space phase encoding direction can fully capture the frequency range of the resting brain, the duration of the k-space phase encoding direction transformation cycle cannot be too long, and the duration of a single k-space phase encoding direction transformation cycle can be controlled to be less than 5 seconds.

[0074] Furthermore, to shorten the time of a single TR (i.e., the time of one k-space phase encoding direction switch), multiband inter-layer acceleration and intra-layer acceleration techniques can be employed. In addition, the aforementioned method of dynamic k-space phase encoding direction switching can be combined with multi-echo EPI technology to reduce interference from motion artifacts, physiological noise (such as heartbeat and respiration), and magnetic field inhomogeneities, thereby further reducing image distortion and signal loss and significantly improving the reliability of resting-state functional connectivity.

[0075] Furthermore, in the aforementioned method of dynamically switching the k-space phase encoding direction, the k-space lines of the echo-plane imaging sequence (EPI) acquired within a single TR (repetition time) include both positive and negative readout directions. Based on the positive or negative readout direction, the k-space lines can be divided into two categories and image reconstruction can be performed separately. Through this reconstruction method, each TR will generate two three-dimensional brain image volumes, thereby doubling the data volume of resting-state functional images. This method can effectively eliminate image ghosting caused by positive and negative readout phase correction errors, significantly improving the reliability of resting-state functional connectivity. In this process, the grouping of functional image data is based not only on the k-space phase encoding direction but also on the positive or negative readout direction, thus doubling the number of groups. Specifically, as... Figure 2 As shown, when the number of phase encoding directions corresponding to the repetition time within a single k-space phase encoding direction transformation cycle is 4, each TR reconstruction generates 2 three-dimensional brain image data, and the final image set has 8 groups; as Figure 3As shown, when the number of k-space phase coding directions corresponding to the repetition time within a single k-space phase coding direction transformation cycle is 2, each TR also reconstructs and generates 2 three-dimensional brain image data, resulting in a final image set with 4 groups. This grouping strategy further optimizes the processing and analysis of image data.

[0076] The following specific embodiment illustrates the implementation process of the resting-state imaging and functional connectivity analysis method provided by the present invention.

[0077] K-space data of the brain in a resting state were acquired using magnetic resonance echo-planar imaging sequences. For example... Figure 2 As shown, in the time dimension, the k-space phase encoding direction corresponding to TR will continuously switch. Each k-space phase encoding direction transformation cycle includes 4 TRs. The switching time, rotation direction, and rotation angle of the k-space phase encoding direction can be flexibly set according to actual needs. The k-space phase encoding directions corresponding to adjacent TRs switch by rotating 90° counterclockwise. Each TR corresponds to 8 k-space lines, but this embodiment does not impose specific limitations on the number of k-space layers and lines corresponding to each TR, and can be adjusted according to actual needs.

[0078] After obtaining the resting-state k-space data of the brain, image reconstruction was performed on each of the resting-state k-space data to obtain functional magnetic resonance imaging (fMRI) images. These fMRI images were then grouped according to the k-space phase encoding direction, with fMRI images corresponding to the same k-space phase encoding direction grouped together. Since there are four phase encoding directions, the fMRI images can be divided into four groups, resulting in four image sets.

[0079] Brain functional connectivity was calculated for each image set to obtain the corresponding functional connectivity results. Then, the average value of the functional connectivity results for the four image sets was calculated, and the calculated average value was taken as the final resting-state functional connectivity result of the brain.

[0080] The following specific embodiment illustrates the implementation process of the resting-state imaging and functional connectivity analysis method provided by this invention. Figure 4 and Figure 5 As shown, image reconstruction was performed on the k-space data of the resting state brain acquired by magnetic resonance echo-planar imaging sequence, and the resulting functional magnetic resonance images were grouped; brain functional connectivity between the two image sets was calculated to obtain the functional connectivity results corresponding to each image set; the average value of the functional connectivity results corresponding to the two image sets was calculated to obtain the final resting state brain functional connectivity results.

[0081] This embodiment is based on a 3T magnetic resonance imaging system and uses the gradient echo planar imaging (EPI) method to acquire functional magnetic resonance images, combined with SMS (simultaneous multi-slice imaging) technology for acceleration, with the SMS acceleration factor set to 3. Specific scanning parameters are as follows:

[0082] -TR / TE (Repetition Time / Echo Time): 1s / 0.03s;

[0083] -FA (flip angle): 65°;

[0084] -FOV (Field of View): 210×210mm 2 ;

[0085] - Intra-layer imaging matrix: 70×70;

[0086] - Number of floors: 48;

[0087] - Voxel Size: 3×3×3mm 3 ;

[0088] - Scan duration: 10 × 6 min.

[0089] During the acquisition process, the phase encoding direction of the echo train of multiple consecutive TRs continuously switches between "up" and "down" (i.e., the rotation angle is 180°), such as... Figure 3 As shown. This design allows images acquired by adjacent TRs to have different distortion characteristics.

[0090] When the k-space phase encoding direction of EPI switches between "up" and "down", the acquired resting-state EPI image is as follows: Figure 4 As shown, the k-space phase encoding direction corresponding to the first TR is "upward," the second TR is "downward," the third TR is "upward," the fourth TR is "downward," and so on. The EPI image set exhibits different forms of image distortion. These differences provide a diverse data foundation for subsequent functional connectivity analysis.

[0091] After acquiring the k-space data of the resting state of the brain, image reconstruction is performed on the k-space data of the resting state of the brain. The resulting functional magnetic resonance images are grouped according to the k-space phase encoding direction. Within each k-space phase encoding direction transformation cycle, the functional magnetic resonance images corresponding to the same k-space phase encoding direction are grouped into a group, resulting in two image sets.

[0092] Brain functional connectivity was calculated for each image set to obtain the corresponding functional connectivity results. Then, the average value of the functional connectivity results for each image set was calculated, and the calculated average value was used as the final resting-state functional connectivity result of the brain.

[0093] like Figure 6 As shown, the trend of resting-state functional connectivity reliability with increasing scan time is illustrated. It can be seen that the reliability of functional connectivity gradually increases with the extension of scan time. Furthermore, the resting-state imaging and functional connectivity analysis method of this invention improves the reliability of functional connectivity compared to traditional methods in existing technologies (which only include a single k-space phase encoding direction). This demonstrates that by using the dynamically switching phase encoding direction acquisition method of this application, combined with diverse functional connectivity analysis strategies, highly reliable resting-state functional connectivity results can be obtained within a relatively short scan time compared to traditional single-phase encoding imaging methods.

[0094] This application achieves brain functional connectivity results corresponding to different phase encoding directions by dynamically switching the phase encoding direction during magnetic resonance brain functional imaging acquisition, without requiring additional scanning sequences and scanning time. Averaging these results significantly improves the reliability and reproducibility of resting-state brain functional connectivity results.

[0095] Compared with the prior art, the technical solution of this application has the following advantages:

[0096] (1) Improved reliability: By integrating the results of multi-k spatial phase encoding directions, the impact of single-direction distortion on functional connectivity is reduced, and more reliable resting-state functional connectivity results are obtained.

[0097] (2) Time efficiency: While achieving high reliability, there is no need to increase the scanning time, avoiding interference caused by field drift or the physiological activities of the subject (such as head movement, large breathing amplitude, etc.) during long-term scanning.

[0098] (3) Flexibility and compatibility: The technical solution of this application can be combined with multi-echo EPI technology, SMS technology and parallel imaging technology to further improve temporal resolution and data quality.

[0099] (4) Wide applicability: The technical solution proposed in this application is of great help to subsequent image processing and result analysis. It is applicable to scientific research and clinical fields and has a wide range of application prospects.

[0100] Figure 7 This is a schematic diagram of the resting-state functional connectivity analysis device provided in the seventh embodiment of the present invention, as shown below. Figure 7As shown, the resting-state functional connectivity analysis device provided in this embodiment of the invention includes a data acquisition unit 701, a grouping unit 702, a calculation unit 703, and an acquisition unit 704, wherein:

[0101] Acquisition unit 701 is used to acquire k-space data of the brain in resting state, which is acquired through magnetic resonance echo-planar imaging sequence; wherein, the magnetic resonance echo-planar imaging sequence includes multiple k-space phase coding direction transformation cycles, each k-space phase coding direction transformation cycle includes multiple repetition times, and the k-space phase coding directions corresponding to the multiple repetition times within each k-space phase coding direction transformation cycle are different from each other, and for any repetition time corresponding to the k-space phase coding direction, there is another repetition time corresponding to the opposite direction of the k-space phase coding direction; grouping unit 702 is used to perform image reconstruction on the k-space of the brain in resting state k-space data respectively, and group the generated functional magnetic resonance images to obtain multiple image sets; calculation unit 703 is used to calculate the brain functional connectivity of each image set to obtain the functional connectivity result corresponding to each image set; obtaining unit 704 is used to calculate the average value of the functional connectivity results corresponding to each image set to obtain the final brain resting state functional connectivity result.

[0102] Specifically, the acquisition unit 701 can acquire k-space data of the brain in a resting state, which can be obtained through a magnetic resonance echo-planar imaging sequence. The magnetic resonance echo-planar imaging sequence includes multiple k-space phase-coding direction transformation cycles, each including multiple repetition times (TRs). Each TR acquires one or more layers of magnetic resonance images. The k-space phase-coding directions corresponding to each TR within each k-space phase-coding direction transformation cycle are different, and within each k-space phase-coding direction transformation cycle, for any repetition time, there exists a k-space phase-coding direction with the opposite direction corresponding to another repetition time. The number of TRs within each k-space phase-coding direction transformation cycle can be set according to actual needs, and this embodiment of the invention does not limit this.

[0103] The grouping unit 702 performs image reconstruction on the k-space data of the resting state of the brain acquired by the magnetic resonance echo planar imaging sequence to obtain functional magnetic resonance images. The obtained functional magnetic resonance images are grouped according to the k-space phase encoding direction to obtain multiple image sets.

[0104] After obtaining the aforementioned multiple image sets, the computing unit 703 calculates brain functional connectivity for each image set, thereby obtaining the functional connectivity results corresponding to each image set. The calculation of brain functional connectivity can employ methods such as correlation analysis, graph theory analysis, and machine learning analysis, selected according to actual needs; this embodiment of the invention does not impose any limitations.

[0105] After obtaining the functional connectivity results corresponding to each set of images, the acquisition unit 704 calculates the average value of the functional connectivity results corresponding to each set of images to obtain the final resting-state functional connectivity results of the brain. By averaging the functional connectivity results corresponding to each set of images, interference caused by a single phase encoding direction can be eliminated, thereby obtaining more reliable resting-state functional connectivity results of the brain.

[0106] The resting-state imaging and functional connectivity analysis device provided in this invention collects resting-state k-space data of the brain. This k-space data is acquired via magnetic resonance echo-planar imaging (MRI) sequences. These MRI sequences include multiple k-space phase-coding direction transformation cycles, each cycle comprising multiple repetition times. The k-space phase-coding directions corresponding to the repetition times within each cycle are distinct, and for any repetition time, there exists an opposite k-space phase-coding direction corresponding to another repetition time. Image reconstruction is performed on the resting-state k-space data, and the resulting functional magnetic resonance images are grouped to obtain multiple image sets. Brain functional connectivity is calculated for each image set to obtain the corresponding functional connectivity result. The average value of the functional connectivity results for each image set is calculated to obtain the final resting-state functional connectivity result. This device can acquire images with different k-space phase-coding directions based on variations in the k-space phase-coding direction. By calculating the average value of functional connectivity, interference caused by a single phase-coding direction can be eliminated, improving the reliability of the functional connectivity results.

[0107] Based on the above embodiments, the grouping unit 702 is further specifically used for:

[0108] The functional magnetic resonance images acquired by the magnetic resonance echo planar imaging sequence are grouped according to the k-space phase encoding direction to obtain multiple image sets. The number of image sets is equal to the number of k-space phase encoding directions within the k-space phase encoding direction transformation period. The number of three-dimensional brain images included in each image set is equal to the number of k-space phase encoding direction transformation periods included in the magnetic resonance echo planar imaging sequence.

[0109] Based on the above embodiments, the k-space phase encoding direction corresponding to each repetition time within each k-space phase encoding direction transformation cycle is rotated sequentially in either a counterclockwise or clockwise direction.

[0110] Based on the above embodiments, the number of repetition times within a single k-space phase encoding direction transformation cycle is further less than a preset value.

[0111] Based on the above embodiments, the duration of the k-space phase encoding direction transformation period is further less than 5 seconds.

[0112] The embodiments of the device provided in this invention can be used to execute the processing flow of the above-described method embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above-described method embodiments.

[0113] Figure 8 This is a schematic diagram of the physical structure of the computer device provided in the eighth embodiment of the present invention, as shown below. Figure 8 As shown, the computer device 600 may include a processor 100 and a memory 140. The memory 140 is coupled to the processor 100. The processor 100 can call logical instructions in the memory 140 to execute the methods provided in the above-described method embodiments, such as: acquiring k-space data of the brain in a resting state, wherein the k-space data of the brain in a resting state is acquired through a magnetic resonance echo-planar imaging sequence; wherein the magnetic resonance echo-planar imaging sequence includes multiple k-space phase coding direction transformation cycles, each k-space phase coding direction transformation cycle includes multiple repetition times, the k-space phase coding directions corresponding to the multiple repetition times within each k-space phase coding direction transformation cycle are different from each other, and for any repetition time corresponding to a k-space phase coding direction, there exists another repetition time corresponding to a k-space phase coding direction with opposite direction; performing image reconstruction on the k-space data of the brain in a resting state, grouping the generated functional magnetic resonance images to obtain multiple image sets; calculating the brain functional connectivity of each image set to obtain the functional connectivity result corresponding to each image set; and calculating the average value of the functional connectivity results corresponding to each image set to obtain the brain resting state functional connectivity result.

[0114] This embodiment discloses a computer program product, which includes a computer program / instructions stored on a computer-readable storage medium. When the computer program / instructions are executed by a computer, the computer can execute the methods provided in the above-described method embodiments, such as: acquiring resting-state k-space data of the brain, wherein the resting-state k-space data of the brain is acquired through a magnetic resonance echo-planar imaging sequence; wherein the magnetic resonance echo-planar imaging sequence includes multiple k-space phase coding direction transformation cycles, each k-space phase coding direction transformation cycle includes multiple repetition times, the k-space phase coding directions corresponding to the multiple repetition times within each k-space phase coding direction transformation cycle are different from each other, and for any repetition time corresponding to a k-space phase coding direction, there exists another repetition time corresponding to a k-space phase coding direction with opposite direction; performing image reconstruction on the resting-state k-space data of the brain respectively, grouping the generated functional magnetic resonance images to obtain multiple image sets; calculating the brain functional connectivity of each image set to obtain the functional connectivity result corresponding to each image set; and calculating the average value of the functional connectivity results corresponding to each image set to obtain the resting-state functional connectivity result of the brain.

[0115] This embodiment provides a computer-readable storage medium storing a computer program / instruction. When the computer program / instruction is executed by a processor, it causes the computer to perform the methods provided in the above-described method embodiments. For example, the methods include: acquiring resting-state k-space data of the brain, wherein the resting-state k-space data is acquired through a magnetic resonance echo-planar imaging sequence; wherein the magnetic resonance echo-planar imaging sequence includes multiple k-space phase coding direction transformation cycles, each k-space phase coding direction transformation cycle includes multiple repetition times, and the k-space phase coding directions corresponding to the multiple repetition times within each k-space phase coding direction transformation cycle are all different, and for any repetition time corresponding to a k-space phase coding direction, there exists another repetition time corresponding to a k-space phase coding direction with opposite direction; performing image reconstruction on the resting-state k-space data of the brain, grouping the generated functional magnetic resonance images to obtain multiple image sets; calculating the brain functional connectivity of each image set to obtain the functional connectivity results corresponding to each image set; and calculating the average value of the functional connectivity results corresponding to each image set to obtain the resting-state functional connectivity results.

[0116] like Figure 8 As shown, the computer device 600 may also include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the computer device 600 does not necessarily need to include these components. Figure 8 All components shown; in addition, computer device 600 may also include Figure 8For components not shown in the figure, refer to existing technologies. It is worth noting that this figure is exemplary; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0117] like Figure 8 As shown, the processor 100, sometimes also referred to as a controller or operation control, may include a microprocessor or other processor device and / or logic device. The processor 100 receives input and controls the operation of various components of the computer device 600.

[0118] The memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The processor 100 may execute the program stored in the memory 140 to perform information storage or processing, etc.

[0119] Input unit 120 provides input to processor 100. Input unit 120 may be, for example, a keypad or touch input device. Power supply 170 provides power to computer device 600. Display 160 displays images and text. Display 160 may be, for example, an LCD display, but is not limited thereto.

[0120] Memory 140 can be a solid-state memory, such as read-only memory (ROM), random access memory (RAM), SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of memory 140 are sometimes referred to as EPROM, etc. Memory 140 can also be some other type of device. Memory 140 includes a buffer 141 (sometimes referred to as buffer memory). Memory 140 may include an application / function storage unit 142 for storing application programs and function programs or processes for executing operations of the computer device 600 via the processor 100.

[0121] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 144 of the memory 140 may include various drivers for the computer device for communication functions and / or for performing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0122] The communication module 110 includes a transmitter / receiver that transmits and receives signals via antenna 111. The communication module 110 is coupled to processor 100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0123] Based on different communication technologies, multiple communication modules 110 can be configured in the same computer device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132, thereby realizing typical telecommunications functions. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 130 is coupled to the processor 100, enabling on-device recording via the microphone 132 and on-device playback of stored sound via the speaker 131.

[0124] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0129] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for resting-state imaging and functional connectivity analysis, characterized in that, include: The resting-state k-space data of the brain is acquired using a magnetic resonance echo-planar imaging sequence. This sequence includes multiple k-space phase encoding direction transformation cycles, each cycle comprising multiple repetition times. The k-space phase encoding directions corresponding to the multiple repetition times within each cycle are distinct, and for any repetition time, there exists an opposite k-space phase encoding direction corresponding to another repetition time. Image reconstruction was performed on the k-space data of the resting state of the brain, and the resulting functional magnetic resonance images were grouped to obtain multiple image sets; Calculate the brain functional connectivity for each image set to obtain the functional connectivity results for each image set; The average value of the functional connectivity results corresponding to each image set was calculated to obtain the resting-state functional connectivity results of the brain.

2. The method according to claim 1, characterized in that, The process involves reconstructing images from the k-space data of the resting state of the brain, and grouping the resulting functional magnetic resonance images to obtain multiple image sets, including: Functional magnetic resonance images obtained by image reconstruction based on the k-space data of the resting state of the brain are grouped according to the k-space phase encoding direction to obtain multiple image sets. The number of image sets is equal to the number of k-space phase encoding directions within the k-space phase encoding direction transformation cycle. The number of three-dimensional brain images included in each image set is equal to the number of k-space phase encoding direction transformation cycles included in the magnetic resonance echo planar imaging sequence.

3. The method according to claim 1, characterized in that, Within each k-space phase encoding direction transformation cycle, the k-space phase encoding direction corresponding to each repetition time rotates sequentially in either a counterclockwise or clockwise direction.

4. The method according to claim 1, characterized in that, The number of repetition times within a single k-space phase encoding direction transformation cycle is less than a preset value.

5. The method according to any one of claims 1 to 4, characterized in that, The duration of the k-space phase encoding direction transformation period is less than 5 seconds.

6. A resting-state imaging and functional connectivity analysis device, characterized in that, include: The acquisition unit is used to acquire k-space data of the brain in a resting state. The k-space data of the brain in a resting state is acquired through a magnetic resonance echo-planar imaging sequence. The magnetic resonance echo-planar imaging sequence includes multiple k-space phase coding direction transformation cycles. Each k-space phase coding direction transformation cycle includes multiple repetition times. The k-space phase coding directions corresponding to the multiple repetition times within each k-space phase coding direction transformation cycle are different from each other, and for any k-space phase coding direction corresponding to any repetition time, there is another k-space phase coding direction corresponding to a repetition time with the opposite direction. The grouping unit is used to perform image reconstruction on the k-space data of the resting state of the brain, and to group the generated functional magnetic resonance images to obtain multiple image sets. The computing unit is used to calculate the brain functional connectivity of each image set and obtain the functional connectivity results corresponding to each image set. The acquisition unit is used to calculate the average value of the functional connectivity results corresponding to each set of images to obtain the final resting-state functional connectivity results of the brain.

7. The apparatus according to claim 6, characterized in that, The grouping unit is specifically used for: Functional magnetic resonance images obtained by image reconstruction based on the k-space data of the resting state of the brain are grouped according to the k-space phase encoding direction to obtain multiple image sets. The number of image sets is equal to the number of k-space phase encoding directions within the k-space phase encoding direction transformation cycle. The number of three-dimensional brain images included in each image set is equal to the number of k-space phase encoding direction transformation cycles included in the magnetic resonance echo planar imaging sequence.

8. The apparatus according to claim 6, characterized in that, Within each k-space phase encoding direction transformation cycle, the k-space phase encoding direction corresponding to each repetition time rotates sequentially in either a counterclockwise or clockwise direction.

9. The apparatus according to claim 6, characterized in that, The number of repetition times within a single k-space phase encoding direction transformation cycle is less than a preset value.

10. The apparatus according to any one of claims 6 to 9, characterized in that, The duration of the k-space phase encoding direction transformation period is less than 5 seconds.

11. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program / instructions that, when executed by a processor, implement the method described in any one of claims 1 to 5.

13. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

Images