Quality control method and equipment for resting-state magnetic resonance cerebrovascular reactivity imaging

Abstract

The present invention discloses a quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging. The quality control method comprises the following steps: first, obtaining a resting-state magnetic resonance cerebrovascular reactivity image queue that needs to be quality controlled; then, quantifying the quality control parameters (DI, Pn, Zscore) of the resting-state magnetic resonance cerebrovascular reactivity image; finally, substituting the obtained quality control parameters into a quality assessment model to obtain a comprehensive index QEI for assessing image quality, selecting a suitable QEI cutoff value based on the distribution of QEI of the resting-state magnetic resonance cerebrovascular reactivity images in the queue and clinical analysis requirements, and eliminating images with a QEI lower than the cutoff value to achieve quality control. The present invention can automatically quantify the quality of resting-state magnetic resonance cerebrovascular reactivity images, thereby providing objective quality control for large-scale clinical data analysis and clinical diagnosis based on resting-state magnetic resonance cerebrovascular reactivity images, and contributing to the clinical transformation of resting-state magnetic resonance cerebrovascular reactivity imaging technology.

Description

Technical Field

[0001] The present invention relates to the fields of magnetic resonance imaging and image processing, and in particular to a quality control method for resting-state magnetic resonance cerebral vascular reactivity imaging. Background Art

[0002] Resting-state MRI cerebrovascular reactivity (CVR) imaging is an emerging MRI technique that utilizes spontaneous fluctuations in blood CO₂ levels during respiration as an intrinsic vasoactive stimulus and simultaneously acquires the BOLD signal to monitor corresponding cerebral hemodynamic changes. This allows for noninvasive and sensitive assessment of vascular health. Compared with traditional CVR imaging techniques based on CO₂ inhalation, resting-state CVR imaging requires less patient compliance, offers increased comfort, and is widely applicable and easily acquired in standard clinical settings. It is particularly advantageous as an imaging marker of cerebrovascular reserve in large-scale clinical data analyses based on public databases such as the Advanced National Institute of Neuroscience (ADNI), UKB, and Human Heart Disease Program (HCP). It can be combined with other clinical biochemical markers and neuropsychological assessments to investigate CVR-related diseases. Resting-state CVR imaging has the potential to provide crucial information for the diagnosis and treatment assessment of cerebrovascular diseases such as acute ischemic stroke, neurodegenerative diseases such as Alzheimer's disease, systemic diseases, and even psychiatric disorders. However, due to the limited spontaneous fluctuations in blood CO2 levels during respiration, resting-state CVR images are susceptible to background physiological noise, resulting in poor signal-to-noise ratio and stability. Poor image quality can adversely affect clinical analysis and diagnosis, necessitating the exclusion of poor-quality CVR images from analysis. Resting-state CVR image quality control based on visual inspection is subjective, particularly in large-scale clinical data analysis, where labor and time costs are high. Therefore, an automated quality control algorithm for resting-state CVR imaging is needed to objectively assess the quality of resting-state CVR images and better leverage the value of resting-state CVR imaging technology in standard clinical settings and large-scale clinical data analysis. Summary of the Invention

[0003] Currently, there is a lack of automatic quality control algorithms for resting-state CVR imaging. To address the problems described in the background technology, the present invention has developed a quality control method for resting-state CVR imaging. Combining the characteristics of poor-quality CVR images, three quality control parameters are extracted from the resting-state CVR images themselves. These parameters are then introduced into the quality assessment model to obtain the comprehensive index QEI for evaluating image quality. This method can automatically quantify the reliability and authenticity of resting-state CVR images in reflecting cerebral vascular reserve, thereby achieving objective quality control and facilitating the clinical transformation of resting-state CVR imaging technology.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging, comprising the following steps:

[0006] S1. Obtain a cohort of resting-state MRI cerebrovascular reactivity images that require quality control. Resting-state MRI cerebrovascular reactivity images are obtained by preprocessing resting-state BOLD image time series and performing voxel-by-voxel general linear regression analysis.

[0007] S2. For each resting-state MRI cerebrovascular reactivity image in the cohort, three quality control parameters are quantitatively calculated: the first quality control parameter is the integrated variance of the resting-state MRI cerebrovascular reactivity values ​​in the gray matter, white matter, and cerebrospinal fluid regions; the second quality control parameter is the proportion of voxels with negative resting-state MRI cerebrovascular reactivity values ​​in the gray matter region; and the third quality control parameter is the whole-brain average Z score converted from the T-score image generated by the general linear regression analysis;

[0008] S3. For each resting-state MRI cerebrovascular reactivity image in the cohort, input the corresponding three quality control parameters into the quality assessment model to obtain a comprehensive assessment index for each image;

[0009] S4. Using the comprehensive evaluation index as a positively correlated representation index of image quality, quality control is performed on all resting-state magnetic resonance cerebrovascular reactivity images in the cohort.

[0010] As a preferred embodiment of the first aspect, in S1, the method for obtaining a resting-state magnetic resonance cerebrovascular reactivity image based on a resting-state BOLD image time series is as follows:

[0011] The resting-state BOLD image time series was subjected to motion correction, spatial smoothing, linear detrending, and temporal filtering in sequence to obtain the BOLD signal time series for each voxel. The BOLD signal time series of all voxels within the whole-brain mask after skull removal were spatially averaged to obtain a reference signal time series. A general linear regression analysis was performed voxel by voxel using the voxel-by-voxel BOLD signal time series as the dependent variable, the reference signal time series as the regression independent variable, and the six motion vectors and a linear trend generated by the motion correction as covariates. The regression coefficient corresponding to the regression independent variable for each voxel was used as the resting-state MRI cerebrovascular reactivity in relative units, thereby obtaining a resting-state MRI cerebrovascular reactivity map in relative units.

[0012] Preferably, the head motion correction, spatial smoothing, linear detrending, time domain filtering and general linear regression analysis are all performed by a Statistical Parametric Mapping tool.

[0013] As a preferred embodiment of the first aspect, the spatial smoothing uses a Gaussian filter with a half-width of 8 mm, and the passband range of the time domain filtering is 0 to 0.1164 Hz.

[0014] As a preferred embodiment of the first aspect, the quantification method of the first quality control parameter is as follows:

[0015] The gray matter mask, white matter mask, and cerebrospinal fluid mask in T1 space were registered to BOLD space. The resting-state MRI cerebrovascular reactivity values ​​of the voxels within the mask were extracted using the three registered masks. The integrated variance DI was then calculated according to the following formula:

[0016]

[0017] Where: V k represents the variance of resting-state MRI cerebrovascular reactivity values ​​within mask k, m k represents the mean value of resting-state MRI cerebrovascular reactivity within mask k, N k represents the number of voxels in mask k. When k = 1, 2, and 3, mask k represents the gray matter mask, white matter mask, and cerebrospinal fluid mask, respectively.

[0018] As a preferred embodiment of the first aspect, the quantification method of the second quality control parameter is as follows:

[0019] The gray matter mask in T1 space was registered to the BOLD space, and the resting-state MRI cerebrovascular reactivity values ​​of the voxels within the mask were extracted using the registered gray matter mask. The proportion Pn of voxels with negative resting-state MRI cerebrovascular reactivity values ​​to the total number of voxels in the gray matter mask was obtained.

[0020] As a preferred embodiment of the first aspect, the quantification method of the third quality control parameter is as follows:

[0021] First, the SPM_T map generated by the current resting-state magnetic resonance cerebrovascular reactivity image in the general linear regression analysis is converted into a Z-score map. Then, the mean Z-score of all voxels within the whole-brain mask range in the Z-score map is calculated, and the mean Z-score is standardized within the cohort to obtain the standardized whole-brain average Z-score Zscore.

[0022] As a preferred embodiment of the first aspect, the quality assessment model is in the form of:

[0023] QEI=k1+k2*DI+k3*Pn+k4*Zscore+k5*Pn*Zscore

[0024] Wherein: k1, k2, k3, k4, and k5 are five fitting parameters, DI, Pn, and Zscore are the first, second, and third quality control parameters, respectively.

[0025] Preferably, the quality assessment model is in the form of:

[0026] QEI=0.6890-0.0083*DI-2.4727*Pn+0.0265*Zscore-0.6329*Pn*Zscore.

[0027] As a preferred embodiment of the first aspect, when quality control is performed using the comprehensive evaluation index QEI, a preset minimum cutoff value of the comprehensive evaluation index QEI is used, and resting-state magnetic resonance cerebrovascular reactivity images in the queue with a comprehensive evaluation index QEI lower than the cutoff value are eliminated as low-quality images.

[0028] In a second aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging as described in any one of the first aspects above is implemented.

[0029] In a third aspect, the present invention provides a computer electronic device comprising a memory and a processor;

[0030] The memory is used to store computer programs;

[0031] The processor is configured to implement the quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging as described in any one of the first aspects above when executing the computer program.

[0032] Compared with the prior art, the present invention has the following technical effects:

[0033] The present invention proposes a quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging, which can automatically quantify the reliability and authenticity of resting-state CVR images in reflecting cerebrovascular reserve. Compared with the existing resting-state CVR image quality control based on visual inspection, the method is more objective, does not require professional imaging experts, and has low manpower and time costs, especially in large-scale clinical data analysis. It provides objective quality control for clinical diagnosis and data analysis based on resting-state CVR as an imaging marker, reduces the adverse effects of poor-quality resting-state CVR, and contributes to the clinical transformation of resting-state CVR imaging technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is the preprocessing flowchart for obtaining CVR images from resting-state BOLD images.

[0035] Figure 2 This is a quantitative flow chart of the quality control parameters DI, Pn, Zscore and the comprehensive quality evaluation index QEI of resting-state CVR images.

[0036] Figure 3 1 and 2 are four typical resting-state CVR images of different qualities and QEI result images obtained using the quality control method developed by the present invention.

[0037] Figure 4 These are correlation result diagrams and specific example diagrams of the test-retest stability verification of the quality control method developed in the present invention using resting-state BOLD data from two scans of the same person in the ADNI3 and HCP public databases. DETAILED DESCRIPTION

[0038] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] The present invention extracts specific quality control parameters from resting-state magnetic resonance cerebrovascular reactivity (CVR) imaging and calculates a comprehensive evaluation index (QEI), thereby achieving automatic quality control. To help those skilled in the art better understand the technical solution of the present invention, the present invention is further described below with reference to the accompanying drawings, and its specific technical effects are demonstrated through examples.

[0040] In an embodiment of the present invention, a quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging is provided, which includes the following steps:

[0041] Step 1. Obtain a cohort of resting-state magnetic resonance cerebrovascular reactivity images that require quality control. Each resting-state magnetic resonance cerebrovascular reactivity image in the cohort is obtained by preprocessing a resting-state BOLD image time series and then performing voxel-by-voxel general linear regression analysis. The magnetic resonance imaging parameters of the resting-state BOLD image time series corresponding to all resting-state magnetic resonance cerebrovascular reactivity images in the cohort are the same.

[0042] Furthermore, a resting-state BOLD image time series consists of a series of resting-state BOLD images acquired at multiple consecutive sampling times. Acquisition of a resting-state BOLD image time series is known in the art, and the process of converting a resting-state BOLD image time series into a resting-state MRI cerebrovascular reactivity image can also be accomplished using existing tools. To facilitate a better understanding for those skilled in the art, the principles of this conversion process in the present invention are described in detail below.

[0043] See also Figure 1As shown in the figure, the resting-state BOLD image time series undergoes motion correction, spatial smoothing, linear detrending, and temporal filtering to generate a BOLD signal time series for each voxel in the image domain. Spatial smoothing uses a Gaussian filter with an 8mm full width at half maximum, and temporal filtering has a passband range of 0–0.1164 Hz to mitigate background physiological noise and extract the component of the BOLD signal most strongly correlated with end-tidal CO2 concentration. Motion correction aligns the BOLD image at each sampling point to the BOLD image at the first sampling point to eliminate motion caused by head motion. This process derives the motion vectors (three displacement vectors and three rotation vectors) and linear trend for each sampling point. Furthermore, the motion-corrected resting-state BOLD image time series undergoes temporal averaging and skull removal to generate a whole-brain mask, which is used to extract voxel-wise signals from brain tissue. Once the whole-brain mask is obtained, the BOLD signal time series of all voxels within the mask are spatially averaged to generate a reference signal time series. Finally, for each voxel in the brain tissue, a general linear regression analysis was performed with its BOLD signal time series as the dependent variable, the reference signal time series as the regression independent variable, and the head motion vector sequence and a linear trend generated by the head motion correction as covariates. The CVR value of that voxel was obtained. After performing general linear regression analysis on each voxel, the whole-brain CVR value was obtained, and then divided by the mean whole-brain CVR value to obtain the resting-state CVR map using relative units.

[0044] In an embodiment of the present invention, the above-mentioned head motion correction, spatial smoothing, linear detrending, time domain filtering and general linear regression analysis can all be implemented by the Statistical Parametric Mapping tool SPM12 in MATLAB.

[0045] The dependent variable, regression independent variable, and covariate in the above general linear regression analysis are all time-domain signals in vector form, so this regression is a time-domain general linear regression analysis. The general linear regression model can be expressed as follows:

[0046] BOLD(t)=β1referencesignal+β2motion+β3t+β0

[0047] Where BOLD(t) is the preprocessed voxel BOLD signal time series, reference signal is the reference signal time series obtained by spatially averaging the BOLD signal time series of all voxels in the whole brain mask, motion is the six head motion vectors generated by head motion correction, and t is the linear trend. β0, β1, β2, and β3 are all regression coefficients, which are given by Get the CVR value of the voxel.

[0048] It's important to note that in the aforementioned time-domain general linear regression analysis, since the regression involves time-domain signals, this effectively amounts to fitting the regression coefficients of the aforementioned general linear regression model using each parameter at each moment as a regression sample, with all regression samples at all moments serving as a sample set. This is, of course, merely to facilitate understanding of the essence of the aforementioned time-domain general linear regression analysis. Software such as MATLAB can implement this time-domain general linear regression analysis directly by inputting data in vector or matrix form, without necessarily requiring pre-splitting the samples at each moment.

[0049] Step 2. For each resting-state MRI cerebrovascular reactivity image in the above-mentioned cohort, three quality control parameters are quantitatively calculated. The first quality control parameter is the integrated variance DI of the resting-state MRI cerebrovascular reactivity values ​​in the gray matter, white matter, and cerebrospinal fluid regions; the second quality control parameter is the proportion Pn of voxels with negative resting-state MRI cerebrovascular reactivity values ​​in the gray matter region; and the third quality control parameter is the whole-brain average Z score Zscore obtained by converting the T-value image generated by the general linear regression analysis.

[0050] See also Figure 2 As shown in FIG, the process of extracting three quality control parameters DI, Pn, and Zscore from CVR images in an embodiment of the present invention is shown. The specific calculation methods of the three parameters are as follows:

[0051] 1) Regarding the calculation of the comprehensive variance DI of the CVR of the three regions of gray matter (GM), white matter (WM), and cerebro-spinal fluid (CSF), CVR varies depending on the tissue type. The maximum or minimum CVR value caused by noise increases the spatial variability of the same tissue, so DI can be used to reflect the noise level. In an embodiment of the present invention, the calculation method of the comprehensive variance DI of the CVR of the three regions is as follows: first, use ANTs software to align the GM, WM, and CSF masks in the T1 space to the BOLD space, and then use the three registered masks to extract the resting-state magnetic resonance cerebrovascular reactivity CVR value of the voxel within the mask, and then calculate the comprehensive variance DI according to the following formula:

[0052]

[0053] Where: V k represents the variance of resting-state MRI cerebrovascular reactivity values ​​within mask k, m k represents the mean value of resting-state MRI cerebrovascular reactivity within mask k, N k represents the number of voxels in mask k. When subscript k = 1, 2, or 3, mask k represents the gray matter mask, white matter mask, and cerebrospinal fluid mask, respectively.

[0054] 2) Calculation of the proportion of voxels with negative CVR values ​​in the gray matter region, Pn. CVR, the ability of blood vessels to dilate when stimulated, is typically positive in gray matter. A large number of voxels with negative CVR values ​​in the gray matter may indicate high noise levels. In an embodiment of the present invention, the proportion of voxels with negative CVR values ​​in the gray matter region, Pn, is calculated as follows: ANTs software is first used to register the GM mask in T1 space to BOLD space. The registered gray matter mask is then used to extract the resting-state MRI cerebrovascular reactivity values ​​of the voxels within the mask. The proportion Pn of voxels with negative resting-state MRI cerebrovascular reactivity values ​​to the total number of voxels within the gray matter mask is then calculated.

[0055] 3) Since resting-state CVR images are generated based on BOLD image time series through general linear regression analysis, the whole-brain average Z-score generated by this process can be used to assess their sensitivity. In an embodiment of the present invention, the whole-brain average Z-score (Zscore) generated by time-domain general linear regression analysis is calculated as follows: first, the SPM_T map generated by the general linear regression analysis of the current resting-state MRI cerebrovascular reactivity image is converted into a Z-score map. In an embodiment of the present invention, the "spm_t2z()" function of the SPM tool can be used to convert the SPM_T map generated by the general linear regression analysis into a Z-score map; then, the mean Z-score of all voxels within the whole-brain mask in the Z-score map is calculated, and this mean Z-score is subjected to intra-group normalization within the resting-state MRI cerebrovascular reactivity image cohort to obtain the normalized whole-brain average Z-score (Zscore).

[0056] It should be noted that the above-mentioned intra-group normalization when calculating the average Z score of the whole brain is to calculate the mean Z score Zscore of each CVR image in the queue. i As the observed value, the Z score is re-standardized and calculated in the entire cohort range. The average of the Z scores corresponding to all CVR images in the entire cohort range is Z mean , the standard deviation of the mean Z score corresponding to all CVR images is Z st , then the Z score mean Zscore corresponding to each CVR image is i The average Z score of the whole brain after re-normalization within the group is Zscore = (Zscore i -Z mean ) / Z st .

[0057] Step 3: For each resting-state MRI cerebrovascular reactivity image in the queue, the corresponding three quality control parameters are input into the quality assessment model to obtain a comprehensive evaluation index QEI for each image.

[0058] Since the three quality control parameters (DI, Pn, and Zscore) obtained in step 2 above reflect image quality in different dimensions, this step requires integrating the three into a single metric (QEI) representing resting-state CVR image quality. The quality control parameters DI, Pn, and Zscore are substituted into the quality assessment model to obtain the comprehensive indicator QEI for evaluating image quality.

[0059] In the present invention, the form of the above-mentioned quality assessment model is not limited, and the QEI that best reflects the image quality is obtained. Since respiratory controlled CVR imaging has an accuracy comparable to that of traditional CVR imaging based on CO2 inhalation, the average structural similarity SSIM between the respiratory controlled CVR image and the resting state CVR image is used as the gold standard (Gold Standard) for evaluating the quality of resting state CVR images. Furthermore, in an embodiment of the present invention, a multivariate linear regression model is performed on the relationship between the three quality control parameters (DI, Pn, Zscore) and the quality assessment gold standard, and a total of four fitting models are tried: (1) "GoldStandard~1+DI+Pn+Zscore" containing only linear terms; (2) "Gold Standard~1+DI+Pn+Zscore+Pn*Zscore" containing linear terms and cross terms after stepwise regression; (3) "Gold Standard~1+DI+Pn+Pn" containing linear terms and square terms after stepwise regression. 2 "; (4) Perform univariate linear regression on the three quality assessment indicators and the quality assessment gold standard respectively, and then calculate the geometric mean of the three univariate linear regressions. Among the four fitting models, the model with the highest Pearson correlation coefficient between the fitted value QEI and the gold standard is "Gold Standard ~ 1 + DI + Pn + Zscore + Pn * Zscore" which contains linear terms and cross terms after stepwise regression.

[0060] In the present invention, the best model form among the above four fitting models is:

[0061] QEI=k1+k2*DI+k3*Pn+k4*Zscore+k5*Pn*Zscore

[0062] Where: k1, k2, k3, k4, and k5 are five fitting parameters, and DI, Pn, and Zscore are three quality control parameters.

[0063] The fitting parameters in the above-mentioned quality assessment model can be obtained by fitting an actual data set. In the embodiment of the present invention, resting-state BOLD images and BOLD images with respiratory control were collected from 17 subjects with three different scanning parameters, and general linear regression analysis was performed to obtain 51 sets of resting-state CVR maps and corresponding respiratory control CVR maps. After fitting, a set of optimal parameters was obtained, as follows:

[0064] QEI=0.6890-0.0083*DI-2.4727*Pn+0.0265*Zscore-0.6329*Pn*Zscore

[0065] Therefore, the quality control parameters DI, Pn, and Zscore are substituted into the above model formula to obtain the comprehensive evaluation index QEI for evaluating image quality.

[0066] Step 4: Using the comprehensive evaluation index QEI as a positively correlated characterization index of CVR image quality, quality control is performed on all resting-state MRI cerebrovascular reactivity images in the cohort.

[0067] It should be noted that the QEI (Quality Evaluation Index) reflects the quality of CVR images; the higher the QEI, the better the CVR image quality. When using the QEI for quality control, the specific quality control method can be set based on the actual application scenario. One implementation method is to use a preset minimum cutoff value for the QEI and then remove resting-state MRI cerebrovascular reactivity images in the cohort with a QEI below this cutoff value as low-quality images.

[0068] The minimum cutoff value of the above-mentioned QEI can be set based on the density distribution of the QEI values ​​of all images in the cohort and the needs of clinical analysis. It can also be set with the help of human assistance such as expert knowledge. For example, the lower quartile or other quantile of the QEI of all CVR image samples can be used as the cutoff value to filter the data.

[0069] To verify the effectiveness of the proposed resting-state CVR image quality evaluation index (QEI) for quality control, the following demonstrates its quality control effects based on steps 1 to 4 of the above-described method, combined with verification experiments, to help those skilled in the art better understand the essence of the present invention.

[0070] Verification experiment 1:

[0071] To qualitatively verify the consistency between the resting-state CVR image quality comprehensive evaluation index (QEI) obtained by the quality control method developed in this invention and the visual inspection results, we designed the following human experiment: 23 healthy subjects were subjected to T1-weighted MPRAGE scanning and resting-state BOLD scanning using a 64-channel head coil. The resting-state BOLD scan was acquired using gradient echo planar imaging. The scanning parameters were as follows: the field of view size was 220 × 220 mm. 2 , the voxel size is 2.50×2.50×2.50mm 3 , the scanning matrix size is 88×88×64, TR / TE=607 / 32ms, 64 slices with a gap of 2.50mm in the axial direction, the phase encoding direction is P to A, the number of measurement time points is 976, and the MB acceleration factor is 8. Figure 3 As shown in FIG, the QEIs calculated using the quality control method developed in the present invention for four typical resting-state CVR images of different qualities are well consistent with the visual inspection results.

[0072] Verification experiment 2:

[0073] To quantitatively validate the applicability of the quality control method developed in this invention for resting-state CVR imaging using resting-state BOLD data from public databases, we performed test-retest stability validation using resting-state BOLD data from two scans of the same individual from ADNI3, approximately one year apart, and two scans of the same individual from HCP, approximately six months apart. CVR maps from healthy individuals scanned within a year of each other do not exhibit significant variability unless due to noise. If both resting-state CVR maps from the same individual are of high quality, test-retest stability (mean structural similarity (SSIM) of the two test CVR maps) will be higher. If at least one CVR map is of poor quality, test-retest stability will decrease. Therefore, if the quality control method developed in this invention can effectively assess the quality of resting-state CVR images, the smaller QEI value of the CVR maps from the same individual scanned twice should be significantly positively correlated with the SSIM of the two maps.

[0074] In the test-retest stability validation 1, 24 sets of resting-state BOLD data were collected from the ADNI3 Axial MB rsfMRI (Eyes Open) scan sequence, which included two scans of the same individual approximately one year apart. The resting-state BOLD scan parameters were as follows: field of view size 220 × 220 mm 2 , the voxel size is 2.50×2.50×2.50mm 3The scanning matrix size is 88×88×64, TR / TE=607 / 32ms, 64 slices with a gap of 2.50mm in the axial direction, the phase encoding direction is P to A, the number of measurement time points is 976, and the MB acceleration factor is 8.

[0075] In the second test-retest stability validation, 45 sets of resting-state BOLD data were collected from the HCP Young Adult cohort, with two scans of the same individual approximately six months apart. The resting-state BOLD scan parameters were as follows: field of view size 208 × 208 mm 2 , the voxel size is 2×2×2mm 3 The scanning matrix size is 104×104×72, TR / TE=720 / 33ms, 72 slices with a gap of 2mm in the axial direction, phase encoding direction is P to A, the number of measurement time points is 1200, and the MB acceleration factor is 8.

[0076] See also Figure 4 (A) The lower QEI in the CVR graphs of the same person tested twice was significantly correlated with the test-retest stability (the average structural similarity (SSIM) of the CVR graphs of the two tests) with a Pearson correlation coefficient of r of 0.8091 (p value much less than 0.001). Figure 4 (B) The lower QEI in the CVR graphs of the same individual tested twice was significantly correlated with the test-retest stability (the average structural similarity (SSIM) of the CVR graphs of the two tests) with a Pearson correlation coefficient of r of 0.8901 (p value much less than 0.001). Figure 4 (C) Schematic diagrams of three typical test-retest stability tests. In this test-retest stability test, the smaller QEI value of the CVR images obtained from two scans of the same individual was significantly positively correlated with the SSIM of both images. This demonstrates that the quality control method developed in this paper for resting-state CVR imaging is applicable to CVR images obtained from resting-state BOLD data of different age groups and with a variety of scanning parameters.

[0077] It should be pointed out that the above embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and replacements made by technicians in this field on the basis of the present invention fall within the scope of protection required by the present invention.

Claims

1. A quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging, characterized in that: The following steps are involved: S1. Obtain a cohort of resting-state MRI cerebrovascular reactivity images that require quality control. Resting-state MRI cerebrovascular reactivity images are obtained by preprocessing resting-state BOLD image time series and performing voxel-by-voxel general linear regression analysis. S2. For each resting-state MRI cerebrovascular reactivity image in the cohort, three quality control parameters are quantitatively calculated: the first quality control parameter is the integrated variance of the resting-state MRI cerebrovascular reactivity values ​​in the gray matter, white matter, and cerebrospinal fluid regions; the second quality control parameter is the proportion of voxels with negative resting-state MRI cerebrovascular reactivity values ​​in the gray matter region; and the third quality control parameter is the whole-brain average Z score converted from the T-score image generated by the general linear regression analysis; S3. For each resting-state MRI cerebrovascular reactivity image in the cohort, input the corresponding three quality control parameters into a quality assessment model to obtain a comprehensive assessment index for each image; the quality assessment model is in the form of: Where: 、 、 、 、 There are 5 fitting parameters, DI, Pn, and Zscore are the first quality control parameter, the second quality control parameter, and the third quality control parameter, respectively; S4. Using the comprehensive evaluation index as a positively correlated representation index of image quality, quality control is performed on all resting-state magnetic resonance cerebrovascular reactivity images in the cohort.

2. The quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to claim 1, wherein: In S1, the method for obtaining a resting-state magnetic resonance cerebrovascular reactivity image based on a resting-state BOLD image time series is as follows: The resting-state BOLD image time series was sequentially subjected to motion correction, spatial smoothing, linear detrending, and temporal filtering to obtain the BOLD signal time series for each voxel. At the same time, the BOLD signal time series of all voxels within the whole-brain mask after skull removal were spatially averaged to obtain a reference signal time series. General linear regression analysis was performed voxel by voxel using the voxel-by-voxel BOLD signal time series as the dependent variable, the reference signal time series as the regression independent variable, and the six motion vectors and a linear trend generated by the motion correction as covariates. The regression coefficient corresponding to the regression independent variable for each voxel was used as the resting-state MRI cerebrovascular reactivity in relative units, thereby obtaining a resting-state MRI cerebrovascular reactivity map in relative units.

3. The quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to claim 2, wherein: The head motion correction, spatial smoothing, linear detrending, temporal filtering, and general linear regression analysis were all performed using the Statistical Parametric Mapping tool.

4. The quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to claim 2, wherein: The spatial smoothing uses a Gaussian filter with a half-width of 8 mm, and the passband range of the time domain filtering is 0-0.1164 Hz.

5. The quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to claim 1, wherein: The first quality control parameter is quantified as follows: The gray matter mask, white matter mask, and cerebrospinal fluid mask in T1 space were registered to BOLD space. The resting-state MRI cerebrovascular reactivity values ​​of the voxels within the mask were extracted using the three registered masks. The integrated variance DI was then calculated according to the following formula: Where: V k represents the variance of resting-state MRI cerebrovascular reactivity values ​​within mask k, m k represents the mean value of resting-state MRI cerebrovascular reactivity within mask k, N k Represents the number of voxels in mask k. When k = 1, 2, and 3, mask k represents the gray matter mask, white matter mask, and cerebrospinal fluid mask, respectively.

6. The quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to claim 1, wherein: The second quality control parameter is quantified as follows: The gray matter mask in T1 space was registered to the BOLD space, and the resting-state MRI cerebrovascular reactivity values ​​of the voxels within the mask were extracted using the registered gray matter mask. The proportion Pn of voxels with negative resting-state MRI cerebrovascular reactivity values ​​to the total number of voxels in the gray matter mask was obtained.

7. The quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to claim 1, wherein: The third quality control parameter is quantified as follows: First, the SPM_T map generated by the current resting-state magnetic resonance cerebrovascular reactivity image in the general linear regression analysis is converted into a Z-score map. Then, the mean Z-score of all voxels within the whole-brain mask range in the Z-score map is calculated, and the mean Z-score is standardized within the cohort to obtain the standardized whole-brain average Z-score Zscore.

8. The quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to claim 1, wherein: The quality assessment model is in the form of: 。 9. The quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to claim 1, wherein: When quality control is performed using the comprehensive evaluation index QEI, the resting-state magnetic resonance cerebrovascular reactivity images in the queue with a comprehensive evaluation index QEI lower than the cutoff value are eliminated as low-quality images based on a preset minimum cutoff value of the comprehensive evaluation index QEI.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to any one of claims 1 to 9 is implemented.

11. A computer electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the quality control method for resting-state magnetic resonance cerebrovascular reactivity imaging according to any one of claims 1 to 9 when executing the computer program.

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