Imaging method for interstitial fluid outflow rate of peripheral gap of brain blood vessel

Abstract

The invention discloses an imaging method for the outflow rate of interstitial fluid in gaps around brain blood vessels. The imaging method comprises the steps that an SWI image and a DTI image are obtained; extracting a vein mask of the whole brain in the SWI image, and calculating a medullary vein direction vector by using a structure tensor vector; extracting a direction vector of a projected fiber and a direction vector of a contact fiber in the DTI image; according to the included angle between the medullary vein direction vector and the x-axis, the included angle between the direction vector of the projection fiber and the z-axis and the included angle between the direction vector of the connection fiber and the y-axis, screening voxel points of interest; for the interested voxel points, the diffusivity of the projection fibers and the diffusivity of the contact fibers in the x-axis direction and the diffusivity of the projection fibers and the diffusivity of the contact fibers in the direction perpendicular to the x-axis direction and the fiber direction are calculated respectively; and calculating the interstitial fluid outflow rate according to the diffusivity. According to the method provided by the invention, the dynamic change of the interstitial fluid (ISF) can be more sensitively captured, and the accuracy of the outflow rate of the interstitial fluid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of magnetic resonance imaging, and in particular to a method for imaging the outflow rate of interstitial fluid in the spaces surrounding brain veins. Background Art

[0002] The glymphatic system is a newly discovered pathway for clearing brain metabolic waste (Iliff, JJ et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloidβ. Sci. Transl. Med. 4, 1–11 (2012)). It effectively eliminates metabolic products including β-amyloid protein through the exchange of cerebrospinal fluid (CSF) along the perivascular space (PVS) and interstitial fluid (ISF). Its dysfunction is believed to be closely related to a variety of neurological diseases. To noninvasively assess the function of this system, Taoka et al. proposed the DTI-ALPS (Diffusion Tensor Imaging–Analysis along the Perivascular Space) method in 2017 (Taoka, T. et al. Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer's disease cases. Jpn. J. Radiol. 35, 172–178 (2017)). This method, based on the perpendicular anatomical relationship between the medullary veins and white matter projection and association fibers in the proximal ventricular zone of the brain, uses diffusion tensor imaging (DTI) to extract information about water molecule diffusion in a specific direction, thereby indirectly assessing ISF flow in the PVS.

[0003] Because it does not require contrast agents, is simple to use, and is easy to acquire data, DTI-ALPS has been widely used in Alzheimer's disease (Taoka, T. et al. Evaluation of glymphatic system activity with thediffusion MR technique: diffusion tensor image analysis along theperivascular space (DTI-ALPS) in Alzheimer's disease cases. Jpn. J. Radiol.35, 172–178 (2017)), Parkinson's disease (Chen, HL et al. Associations among CognitiveFunctions, Plasma DNA, and Diffusion Tensor Image along the PerivascularSpace (DTI-ALPS) in Patients with Parkinson's Disease. Oxid. Med. Cell.Longev. 2021, 4034509 (2021)) (Wood, KH et al. Diffusion Tensor Imaging-Along the Perivascular-Space Index Is Associated with Disease Progression inParkinson's Disease. Mov. Disord. 39, 1504–1513 (2024)), small vessel disease (Tian, ​​Y. etal. Impaired glymphatic system as evidenced by low diffusivity alongperivascular spaces is associated with cerebral small vessel disease: apopulation-based study. Stroke Vasc. Neurol. 8, 413–423 (2023)) (Yang, J. etal.Association of glymphatic clearance function with imaging markers and risk factors of cerebral small vessel disease. J. Stroke Cerebrovasc. Dis. 34, 108187 (2025)) and other brain diseases and aging (Steward, CE et al. Assessment of the DTI-ALPS Parameter Along the Perivascular Space in Older Adults at Riskof Dementia. J. Neuroimaging 31, 569–578 (2021)).

[0004] However, DTI-ALPS still faces two key challenges in practical applications: First, existing algorithms struggle to accurately select voxels that meet the perpendicular relationship between blood vessels and fibers, leading to underestimation of DTI-ALPS values; second, traditional methods rely on manual selection of ROIs, which is subject to significant subjectivity and poor reproducibility. These issues severely impact the accuracy and reliability of DTI-ALPS in assessing cerebral lymphatic function and urgently require improvement. Summary of the Invention

[0005] The present invention provides an imaging method for the interstitial fluid outflow rate in the spaces surrounding brain blood vessels, which can more sensitively capture the dynamic changes of interstitial fluid (ISF) and improve the accuracy of the interstitial fluid outflow rate.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A method for imaging the outflow rate of interstitial fluid in the perivascular space of the brain, the imaging method comprising the following steps: (1) Obtain SWI images and DTI images of the head using a magnetic resonance imaging system; (2) Extract the venous mask of the whole brain in the SWI image, and select the area beside the lateral ventricle as the region of interest. The corresponding venous mask is the medullary vein mask u, and the medullary vein direction vector is calculated using the structural tensor vector; (3) Extract the direction vectors of the projection fibers and the direction vectors of the communication fibers in the DTI image; (4) Define the angle between the direction vector of the medullary vein and the x-axis as α, the angle between the direction vector of the projection fiber and the z-axis as β1, and the angle between the direction vector of the communication fiber and the y-axis as β2. Filter the voxel points of interest based on α, β1, and β2; (5) For each voxel of interest, calculate the diffusion rate D of the projected fiber in the x-axis direction. x,projection and the diffusion rate D of the contact fiber in the x-axis direction x,association , and calculate the diffusion rate D of the projected fiber in the z-axis direction z,projection and the diffusion rate D of the contact fiber in the y-axis direction y,association ; (6) Calculate the ratio of the diffusivity of the projected fiber in the x-axis direction to the diffusivity in the z-axis direction, D x,projection / D z,projection , and the ratio of the diffusion rate of the connecting fiber in the x-axis direction to the diffusion rate in the y-axis direction D x,association / D y,association , and then take the average of the two as the interstitial fluid outflow rate, recorded as DTI-ALPS-Plus.

[0007] The above method includes four parts: vein segmentation, vein direction vector reconstruction, fiber direction vector reconstruction, screening of vertical pixel points between the paraventricular medullary vein and fiber bundle, and calculation of DTI-ALPS parameters.

[0008] In step (1), the acquired SWI image is interpolated into isotropic data based on the dimension with the smallest resolution.

[0009] In step (1), the acquired DTI image is interpolated so that its resolution is consistent with the resolution of the SWI image; and the acquired DTI image is preprocessed, wherein the preprocessing is selected from head motion correction, eddy current correction or EPI correction.

[0010] In step (2), the whole-brain vein mask includes a whole-brain vein mask of magnetic resonance visible blood vessels.

[0011] In step (2), the method for calculating the vein direction vector of the visible blood vessels in the region of interest using the structure tensor vector is: (2-1) Perform Gaussian filtering with kernel σ on the vein mask u of the visible blood vessels to obtain ; (2-2) After filtering, the gradient g along the x, y and z directions are calculated respectively x 、g y and g z , and construct the structure tensor matrix J: = ; (2-3) Gaussian filtering with kernel ρ is performed on the structure tensor matrix J to obtain the semi-positive definite matrix ; (2-4) For semi-positive definite matrices Find the eigenvalue and get μ1≥μ2≥μ3. The corresponding eigenvectors are w1, w2, and w3 respectively. The eigenvector w3 corresponding to the minimum eigenvalue is the direction vector of the medullary vein.

[0012] Furthermore, the value range of σ is 0.5-2, and the value range of ρ is 1-3.

[0013] Furthermore, the calculation of the gradient value in the x-direction is defined as the signal value of the current voxel minus the signal value of the adjacent voxel in the positive direction of the x-axis, the calculation of the gradient value in the y-direction is defined as the signal value of the current voxel minus the signal value of the adjacent voxel in the positive direction of the y-axis, and the calculation of the gradient value in the z-direction is defined as the signal value of the current voxel minus the signal value of the adjacent voxel in the positive direction of the z-axis.

[0014] Furthermore, in step (3), a linear least squares algorithm is used to fit the DTI image to obtain a tensor matrix, in which the first eigenvalue is the direction vector of the reconstructed projection fiber and the connection fiber.

[0015] In the present invention, the range of α and β can be selected based on the overall data quality, so that the fibers and the medullary vein can be as close to perpendicular as possible, and the maximum number of voxels can be screened under this condition to ensure the presence of DTI-ALPS-Plus for each subject. Furthermore, in step (4), voxels with α≤15° and β1≤15° and voxels with α≤15° and β2≤15° are selected as voxel points of interest.

[0016] The present invention also discloses the application of DTI-ALPS-Plus obtained by the imaging method of the interstitial fluid outflow rate in the perivascular spaces of the brain in the functional evaluation of the cerebral lymphatic circulatory system.

[0017] Compared with the prior art, the present invention has the following excellent effects: The method provided by the present invention proposes a new method to use DTI-ALPS-Plus as the interstitial fluid outflow rate of the perivascular space of the brain based on a mask composed of voxels where fibers and veins are perpendicular to each other, so as to more sensitively capture the dynamic changes of interstitial fluid (ISF); The method provided by the present invention can automatically screen nerve fiber pixels perpendicular to the direction of veins; based on the effective pixels obtained by screening, the DTI-ALPS index is further calculated, thereby effectively overcoming the problem of underestimation of values ​​caused by inaccurate vessel direction identification in traditional methods; The interstitial fluid outflow rate of perivascular spaces in the brain obtained by the method provided by the present invention is consistent and stable in repeated scans, and also exhibits higher reproducibility between different observers. The present invention compared the intraclass correlation coefficient (ICC) of DTI-ALPS-Plus and DTI-ALPS, and the results showed that DTI-ALPS-Plus exhibited higher stability in both inter-observer and intra-observer agreement. The interstitial fluid outflow rate of the perivascular spaces in the brain obtained by the method provided by the present invention shows a stronger ability to distinguish between patients with Alzheimer's disease and normal people, significantly improving the accuracy and practical value of clinical applications: The present invention applies the improved methods to subjects with normal cognition (NC) and Alzheimer's disease (AD), respectively. The results show that compared with DTI-ALPS, DTI-ALPS-Plus can not only more significantly distinguish between normal cognition (NC) and Alzheimer's disease (AD), but also more effectively distinguish between normal cognition (NC) and mild cognitive impairment (MCI) groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the magnetic resonance imaging method for measuring the interstitial fluid flow rate in the space surrounding the medullary veins of the lateral ventricles in Example 1; Figure 2 This is a flow chart of vein direction vector reconstruction in Example 1; Figure 3 Comparison of the consistency between the DTI-ALPS-Plus and DTI-ALPS scan-repeat scans obtained in Example 1; Figure 4 Comparison of interobserver agreement between DTI-ALPS-Plus and DTI-ALPS obtained in Example 1; Figure 5 Comparison of the AD disease diagnostic capabilities of DTI-ALPS-Plus and DTI-ALPS obtained in Example 1. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0020] The magnetic resonance sequences used in the present invention include SWI sequences and DTI sequences, such as Figure 1 As shown. Regarding the SWI sequence, the experimental design process should consider that it should be as close to isotropy as possible while ensuring resolution to reduce the inhomogeneity of the gradients calculated in different directions; regarding the DTI sequence, the minimum b value should be 0 s / mm 2 , the maximum b value is 1000 s / mm2 , to filter out the influence of venous blood on magnetic resonance signals.

[0021] Example 1 As a specific embodiment, this embodiment is a magnetic resonance imaging method for measuring the interstitial fluid flow rate in the space around the medullary vein of the lateral ventricle on the brain of healthy adult subjects. The operation flow chart is shown in FIG. Figure 1 and Figure 2 , specifically including the following steps: Step 1: Place healthy adult subjects and Alzheimer's disease patients in a 3T MRI system. Capture head images with the center of the head as the scanning center. This example collected susceptibility-weighted imaging (SWI) and differential tactile imaging (DTI) MRI data from 50 healthy adult subjects, 24 patients with mild cognitive impairment (MCI), and 24 patients with dementia.

[0022] Among them, during the SWI acquisition stage, the closer the data resolution is controlled to isotropy, the better the algorithm effect will be. Therefore, the experimental design should try to consider making the data as close to isotropy as possible while the resolution is clear enough.

[0023] Step 2: Set the resolution of the SWI sequence to 0.6 × 0.6 × 1.2 mm 3 The isotropic interpolation is performed according to the minimum value of the voxel in the three dimensions, that is, the SWI is interpolated to 0.6×0.6×0.6 mm 3 The Frangi algorithm is applied to extract the vein mask of the whole brain.

[0024] The Frangi algorithm should set an appropriate threshold to control the strictness of the medullary vein segmentation. The larger the threshold, the stricter the standard, and the fewer medullary vein masks are obtained. In this embodiment, the threshold of the Frangi algorithm can be selected from 0.004 to 0.008.

[0025] Step 3: Using the whole brain vein mask obtained in step 1, the medullary vein is selected as the region of interest, defined as u. First, a Gaussian filter with kernel σ is applied to u and defined as u. σ , and then calculate u separately σ Gradient g along the x, y, and z directions x 、g y and g z , and construct the structure tensor matrix J, where: =

[0026] And perform Gaussian filtering with kernel ρ, that is

[0027] For semi-positive definite matrices By calculating the eigenvalue, we get μ1≥μ2≥μ3, and the corresponding eigenvectors are w1, w2, and w3, respectively. The eigenvector w3 corresponds to the minimum eigenvalue. The direction corresponding to the maximum eigenvalue is the direction in which the magnetic resonance signal changes most dramatically, and the direction corresponding to the minimum eigenvalue is the direction in which the magnetic resonance signal changes most slowly, that is, w3 is the direction vector of the medullary vein.

[0028] Among them, the calculation of the gradient value in the x-direction is defined as the signal value of the current voxel minus the signal value of the adjacent voxel in the positive direction of the x-axis, the calculation of the gradient value in the y-direction is defined as the signal value of the current voxel minus the signal value of the adjacent voxel in the positive direction of the y-axis, and the calculation of the gradient value in the z-direction is defined as the signal value of the current voxel minus the signal value of the adjacent voxel in the positive direction of the z-axis.

[0029] The appropriate filter kernel size can be selected according to the SWI image resolution. In this embodiment, σ represents the filter kernel for the SWI image and is selected from 0.5 to 2. ρ represents the filtering of the SWI image gradient space and is selected from 1 to 3.

[0030] Step 4: Interpolate the DTI image to ensure that its resolution is consistent with that of the SWI, and perform preprocessing operations such as head motion correction, eddy current correction, and EPI correction.

[0031] Step 5: Use the linear least squares algorithm to fit the DTI data to obtain a tensor matrix, where the first eigenvalue is the direction vector of the reconstructed projection fibers and communication fibers.

[0032] Step 6: Calculate the angle between the blood vessel direction vector obtained in step 3 and the x-axis and define it as α; and the angle between the projection fiber and the z-axis / the angle between the communication fiber and the y-axis obtained in step 5 and define them as β1 and β2 respectively.

[0033] Step 7: Voxels with α values ​​less than or equal to 15° and β (β1 and β2) values ​​less than or equal to 15° in step 6 were selected as the regions of interest for DTI-ALPS-Plus to ensure the perpendicularity of the selected fibers and vessels between the regions of interest.

[0034] Step 8: For the voxel points of interest within the projection fiber, calculate the diffusion coefficient of these voxel points along the X direction, which is defined as D x,projection , and the diffusion coefficient D along the Y direction y,projection ; For the voxel points of interest within the contact fiber, calculate the diffusion coefficient of these voxel points along the X direction, defined as D x,association , and the diffusion coefficient D along the Z direction z,association ; Step 9: For the region of interest selected in step 7, the diffusion rate along the x-axis calculated in step 8 is divided by the diffusion rate along the direction perpendicular to both the x-axis and the fiber (the y-direction for the projection fiber and the z-direction for the contact fiber). That is, for the projection fiber, calculate D x,projection / D y,projection ; For the connection fiber, calculate D x,association / D z,association ; Take D x,projection / D y,projection and D x,association / D z,association The average value of is taken as the DTI-ALPS-Plus value.

[0035] To demonstrate the repeatability and diagnostic capability of the present invention for Alzheimer's disease, the experimental results of this specific embodiment will be described below with reference to the accompanying drawings: Since only 10 of the 50 healthy subjects had repeated scan data, after the first scan, these 10 healthy subjects were asked to leave the scanning room and walk freely for 2 minutes, and then returned to the scanning room for a second scan. This ensured that the patient's head position was not exactly the same during the two scans, and that the same clinician would delineate the DTI-ALPS-Plus region of interest for the two scans. Figure 3 It was demonstrated that DTI-ALPS-Plus has better scan-to-re-scan consistency than DTI-ALPS.

[0036] In addition, two clinicians were invited to draw the DTI-ALPS-Plus region of interest for 50 healthy subjects, and the consistency between observers was calculated. Figure 4 It was demonstrated that DTI-ALPS-Plus had better interobserver agreement than DTI-ALPS.

[0037] Finally, in order to compare the diagnostic ability of the two methods for Alzheimer's disease, they were applied to 50 healthy subjects, 24 subjects with mild cognitive impairment, and 24 subjects with dementia. Figure 5 It was demonstrated that DTI-ALPS-Plus can more accurately diagnose patients with Alzheimer's disease and is more sensitive in identifying patients with mild cognitive impairment.

[0038] The method provided by the present invention automates the calculation of cerebral venous orientation and, based on this, automatically selects pixels whose venous orientation is perpendicular to the orientation of nerve fibers. The DTI-ALPS values ​​calculated based on the automatically selected valid pixels can effectively overcome the underestimation of values ​​caused by inaccurate vascular orientation identification in traditional methods. Furthermore, the present invention also achieves semi-automation of the DTI-ALPS calculation process by optimizing the original method of selecting only a portion of fibers as the region of interest (ROI) to selecting all relevant fiber regions as the basis for calculation. Furthermore, an automated screening mechanism is introduced to replace the manual delineation of the ROI, effectively reducing the subjectivity and poor repeatability associated with human intervention. In summary, the present invention significantly improves the stability and diagnostic accuracy of the DTI-ALPS method in evaluating brain lymphatic circulatory system function.

Claims

1. A method for imaging the outflow rate of interstitial fluid in the perivascular space of the brain, characterized in that: The imaging method comprises the following steps: (1) Obtain SWI and DTI images of the head using a magnetic resonance imaging system; (2) Extract the venous mask of the whole brain in the SWI image, and select the area beside the lateral ventricle as the region of interest. The corresponding venous mask is the medullary vein mask u, and the medullary vein direction vector is calculated using the structural tensor vector; (3) Extract the direction vectors of the projection fibers and the direction vectors of the communication fibers in the DTI image; (4) Define the angle between the direction vector of the medullary vein and the x-axis as α, the angle between the direction vector of the projection fiber and the z-axis as β1, and the angle between the direction vector of the communication fiber and the y-axis as β2. Filter the voxel points of interest based on α, β1, and β2; (5) For each voxel of interest, calculate the diffusion rate D of the projected fiber in the x-axis direction. x,projection and the diffusion rate D of the contact fiber in the x-axis direction x,association , and calculate the diffusion rate D of the projected fiber in the z-axis direction z,projection and the diffusion rate D of the contact fiber in the y-axis direction y,association ; (6) Calculate the ratio of the diffusivity of the projected fiber in the x-axis direction to the diffusivity in the z-axis direction, D x,projection / D z,projection , and the ratio of the diffusion rate of the contact fiber in the x-axis direction to the diffusion rate in the y-axis direction D x,association / D y,association , and then take the average of the two as the interstitial fluid outflow rate, recorded as DTI-ALPS-Plus.

2. The method for imaging the outflow rate of interstitial fluid in the perivascular space of the brain according to claim 1, characterized in that: In step (1), the acquired SWI image is interpolated into isotropic data based on the dimension with the smallest resolution.

3. The imaging method for the outflow rate of interstitial fluid in the perivascular space of the brain according to claim 1, characterized in that: In step (1), the acquired DTI image is interpolated so that its resolution is consistent with the resolution of the SWI image; and the acquired DTI image is preprocessed, wherein the preprocessing is selected from head motion correction, eddy current correction or EPI correction.

4. The method for imaging the outflow rate of interstitial fluid in the perivascular space of the brain according to claim 1, characterized in that: In step (2), the method for calculating the vein direction vector of the visible blood vessels in the region of interest using the structure tensor vector is: (2-1) Perform Gaussian filtering with kernel σ on the vein mask u of the visible blood vessels to obtain ; (2-2) After filtering, the gradient g along the x, y and z directions are calculated respectively x 、g y and g z , and construct the structure tensor matrix J: = ； (2-3) Gaussian filtering with kernel ρ is performed on the structure tensor matrix J to obtain the semi-positive definite matrix ; (2-4) For semi-positive definite matrices By calculating the eigenvalue, we get μ1≥μ2≥μ3, and the corresponding eigenvectors are w1, w2, and w3 respectively. The eigenvector w3 corresponding to the minimum eigenvalue is the direction vector of the medullary vein.

5. The method for imaging the outflow rate of interstitial fluid in the perivascular space of the brain according to claim 4, characterized in that: The value range of σ is 0.5-2, and the value range of ρ is 1-3.

6. The method for imaging the outflow rate of interstitial fluid in the perivascular space of the brain according to claim 4, characterized in that: The calculation of the gradient value in the x-direction is defined as the signal value of the current voxel minus the signal value of the voxel adjacent in the positive direction of the x-axis. The calculation of the gradient value in the y-direction is defined as the signal value of the current voxel minus the signal value of the voxel adjacent in the positive direction of the y-axis. The calculation of the gradient value in the z-direction is defined as the signal value of the current voxel minus the signal value of the voxel adjacent in the positive direction of the z-axis.

7. The method for imaging the outflow rate of interstitial fluid in the perivascular space of the brain according to claim 1, characterized in that: In step (3), the linear least squares algorithm is used to fit the DTI image to obtain a tensor matrix, in which the first eigenvalue is the direction vector of the reconstructed projection fiber and the connection fiber.

8. The method for imaging the outflow rate of interstitial fluid in the perivascular space of the brain according to claim 1, characterized in that: In step (4), voxels with α≤15° and β1≤15° and voxels with α≤15° and β2≤15° are selected as voxel points of interest.

9. Use of DTI-ALPS-Plus obtained by the imaging method for the interstitial fluid outflow rate of perivascular spaces in the brain according to any one of claims 1 to 8 in the evaluation of brain lymphatic circulatory system function.

Images