Method for evaluating carotid artery stenosis and postoperative brain structure change based on NODDI model

Abstract

The invention is suitable for the technical field of medical imaging, and provides a method for evaluating carotid artery stenosis and postoperative brain structure change based on an NODDI model. According to the method, the influence of the cerebral perfusion change of the carotid artery stenosis patient on the brain microstructure is monitored in vivo through the NODDI model. By using Vic, Viso and ODI data provided by the NODDI model, in the preclinical research and practical application, on the premise of not influencing the normal course of the disease, the information of brain microstructure change is dynamically and repeatedly provided in a living body, and the characteristic difference between the preoperative stenosis side and contralateral side of a patient with carotid artery stenosis and the characteristic difference between the stenosis side CEA preoperative and postoperative partial white matter areas of the brain of the patient with carotid artery stenosis are obtained. Meanwhile, the abnormal cerebral white matter microstructure of the stenosis side of the patient suffering from the carotid artery stenosis is researched, the change of the cerebral white matter microstructure before and after CEA is associated with postoperative cerebral blood flow recovery, cerebral perfusion and cognitive improvement, and the problem that the cerebral white matter microstructure of the patient suffering from the carotid artery stenosis cannot be displayed through a traditional imaging technology is solved.

Description

Technical Field

[0001] The present invention belongs to the field of medical imaging technology, and in particular relates to a method for evaluating carotid artery stenosis and postoperative brain structural changes based on the NODDI model. Background Art

[0002] The Global Burden of Disease Study shows that stroke is the second leading cause of death in the world, second only to coronary heart disease, and is also the third leading cause of disability and death. The sequelae of non-fatal stroke can also significantly reduce the patient's ability and quality of life. At present, the incidence of stroke continues to rise, bringing tremendous pressure to the world. Among them, ischemic stroke is particularly common, mainly caused by stenosis of the extracranial carotid artery, and atherosclerosis is its common cause. Carotid artery plaques change hemodynamics through severe stenosis, leading to low perfusion of corresponding parts of the brain, or microemboli in the plaque and / or microthrombi on the surface fall off and enter the brain with blood, causing occlusion of microvessels. Both pathways can cause cerebral ischemia. There is no consensus on which mechanism is more closely related to the symptoms of cerebral ischemia, but most opinions believe that the stenosis and plaque morphological characteristics work together to induce neurological symptoms.

[0003] Drug therapy is mainly used to delay the development of carotid artery plaques, but it cannot restore blood flow. Carotid endarterectomy (CEA) and carotid artery stening (CAS) are currently common surgical options in clinical practice. CEA directly removes plaques, which fundamentally restores brain tissue blood flow and improves perfusion. Magnetic resonance imaging (MRI) is an important auxiliary tool in the diagnosis and treatment of stroke, and plays an important role in the identification and staging of ischemic and reversible brain tissue damage. Diffusion tensor magnetic resonance imaging (DTI) is an imaging technique widely used in central nervous system research, but it is not sensitive to the complexity of white matter microstructure. White matter damage may lead to neurocognitive and behavioral disorders. Traditional DTI indicators such as fractional anisotropy and mean diffusivity can only describe the diffusion situation statistically, and do not directly correspond to the parameters with biophysical significance of the subordinate tissues. Diffusion kurtosis imaging (DKI), as an extension of DTI, is a water-based non-Gaussian diffusion MRI imaging technique that can reflect the complexity of tissue microstructure to a certain extent, especially in areas with high brain tissue heterogeneity, but it still lacks specificity and is an indirect characterization of microstructure. Neither DTI nor DKI can accurately distinguish the effects of confounding factors such as neurite density and orientation dispersion on microstructural changes. The application of multimodal MRI can reveal brain structure, function, changes in cerebral blood flow, and white matter integrity.

[0004] The Neurite Orientation Dispersion and Density Imaging (NODDI) model is a three-compartment diffusion model with multiple shells and high angular resolution. It estimates biologically relevant parameters based on the "tissue model" and represents the morphological information of nerve fibers by reflecting the complexity of the microstructure of axons and dendrites. The NODDI model divides the water diffusion in brain tissue into intracellular, extracellular and free water, and stabilizes the fitting process by constraining four diffusion rate parameters. It also retains the intracellular volume fraction (Vic) representing the axon density, the orientation dispersion index (ODI) representing the dispersion of axons and dendrites, and the isotropic volume fraction (Viso) representing the interstitial free water, which has better discrimination between the intracellular and extracellular tissues of neurons and cerebrospinal fluid.

[0005] Based on the above background, the present invention proposes a method for evaluating carotid artery stenosis and postoperative brain structural changes based on the NODDI model. Summary of the invention

[0006] The purpose of the present invention is to provide a method for evaluating carotid artery stenosis and postoperative brain structural changes based on the NODDI model, aiming to solve the problems raised in the above background technology.

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] The method for evaluating carotid artery stenosis and postoperative brain structural changes based on the NODDI model includes the following steps:

[0009] Step S1, image data acquisition;

[0010] Whole-brain DWI and NODDI scans were performed using a Philips 3.0T Ingenia MR scanner to obtain dicom sequences;

[0011] Step S2, image data processing, specifically includes the following steps:

[0012] Step 21, check the image quality parameters, pre-process the raw data that meet the requirements, and obtain the brain mask of the nii file and the b0 image;

[0013] Step 22, input the brain tissue mask file and the nii file into the NODDI model to obtain the microstructure information of the whole brain, including Vic value, Viso value and ODI value;

[0014] Step 23, using ITK-SNAP software to perform regional segmentation on the NODDI image, with special attention paid to the white matter area, and dividing it into three areas: basal ganglia, corona radiata, and centrum semiovale, and obtaining Vic values, Viso values, and ODI values ​​of the three areas at different time periods;

[0015] Step 24: Perform comparative analysis using the values ​​obtained in step S23 to evaluate carotid artery stenosis and postoperative changes in brain structure.

[0016] Furthermore, in step S1, when a Philips 3.0T Ingenia MR scanner is used for scanning, the scanning coil is a 16-channel head coil, and a multi-slice single-shot spin echo planar pulse sequence along 32 isotropic diffusion gradient directions is used to perform whole-brain DWI and NODDI scans.

[0017] Furthermore, in step 21, the specific operation of preprocessing is: using the mricron tool to convert the scanned DICOM sequence into 4D Nifti format to obtain a nii file, and then using FDT in FSL to perform eddy current correction, including head motion eddy current and gradient direction correction, to obtain the brain mask of the b0 image from the processed data.

[0018] Furthermore, in step 23, the different time periods are before the operation, 3 days before the operation and 3 days after the operation, and 3 months before the operation and 3 months after the operation.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention adopts the NODDI model to solve the problem that traditional imaging technology cannot show the microstructure of brain white matter in patients with carotid stenosis. The NODDI model is used to monitor the effect of changes in brain perfusion on brain microstructure in patients with carotid stenosis. Using the Vic, Viso and ODI data provided by the NODDI model, in preclinical research and practical applications, the information on brain microstructure changes is provided in vivo, dynamically and repeatably without affecting the normal course of the disease. Then, through statistical analysis, the characteristic differences of some brain white matter areas before and after CEA on the stenotic side and the contralateral side of patients with carotid stenosis were obtained. At the same time, the abnormalities of brain white matter microstructure on the stenotic side of patients with carotid stenosis were studied, and the changes in brain white matter microstructure before and after CEA were associated with postoperative cerebral blood flow recovery, cerebral perfusion and cognitive improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0022] In order to have a clearer understanding of the technical features, purposes and beneficial effects of the present invention, the technical solution of the present invention is now described in detail below, but it should not be construed as limiting the applicable scope of the present invention.

[0023] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0024] One embodiment of the present invention provides a method for evaluating carotid artery stenosis and postoperative brain structural changes based on the NODDI model. The method flow chart is as follows: Figure 1 As shown, the method specifically comprises the following steps:

[0025] 1. Selection of research subjects;

[0026] 1.1 Inclusion criteria:

[0027] 1) Preoperative cervical CT angiography (CTA) examination diagnosed as moderate to severe carotid artery stenosis (symptomatic: stenosis ≥50%, or asymptomatic: stenosis ≥70%).

[0028] 2) The patient meets the surgical indications, and the patient and his / her family voluntarily choose CEA treatment and sign the informed consent form for surgery, and there are no contraindications to surgery.

[0029] 3) Perform MRI scans, vascular ultrasound and other related examinations on the 3rd day and 3 months after surgery.

[0030] 4) Have an education level of more than 6 years and can cooperate with the cognitive scale assessment smoothly.

[0031] 1.2 Exclusion criteria:

[0032] 1) There are contraindications to MRI examination, such as claustrophobia.

[0033] 2) Severe cardiopulmonary insufficiency exists and CEA cannot be performed or the family members and patients choose to undergo CAS.

[0034] 3) The patient has a history of severe systemic genetic or acquired diseases, especially severe mental / neurological diseases and / or severe stroke.

[0035] 4) Carotid artery stenosis caused by reasons other than atherosclerosis.

[0036] 5) The degree of contralateral carotid artery stenosis is ≥50%.

[0037] 6) Presence of dementia or difficulty complying with cognitive assessments, such as severe hearing or language impairment.

[0038] 7) Brain tumor.

[0039] Finally, 40 patients were included.

[0040] 2. Image data acquisition;

[0041] All images were acquired using a Philips 3.0T Ingenia MR scanner with a 16-channel head coil. Whole-brain diffusion-weighted imaging (DWI) and NODDI scanning were performed using a multi-slice single-shot spin echo planar pulse sequence (EPI) along 32 isotropic diffusion gradient directions.

[0042] The scanning parameters are as follows:

[0043] 1) DWI scan: 3 b values ​​are 0, 1000, and 1500 s / mm 2, layer thickness 2.43mm, interval 5mm, TR=3000ms, TE=90ms, matrix 100×128, field of view (FOV) 200mm×220mm×110mm, voxel 2mm×2mm×3mm, scanning time 56s.

[0044] 2) NODDI scan: layer thickness 4 mm, interval 0 mm, TR = 3390 ms, TE = 104 ms, matrix 118 × 118, field of view (FOV) 200 mm × 200 mm × 100 mm, voxel 2 mm × 2 mm × 4 mm, scanning time 336 s.

[0045] The total scanning time was 727 seconds. The DICOM sequence was automatically generated after the scan, and data with severe motion artifacts were excluded. The patients were scanned with the same parameters before surgery, 3 days after surgery, and 3 months after surgery.

[0046] 3. Image data processing;

[0047] 1) Check the image quality parameters, including the number of gradient directions, signal-to-noise ratio, head motion, artifacts, etc., to ensure that the data meets the requirements, and then pre-process the raw data that meets the requirements (clear image structure, good contrast, low noise, no motion artifacts and other artifacts, etc.).

[0048] 2) Preprocessing: The scanned DICOM sequence was converted into 4D Nifti format using the mricron tool to obtain a nii file. Eddy current correction was performed using FDT (FMRIB's Diffusion Toolbox, FDT) in FSL (FMRIB Software Library, FSL), including head motion eddy current and gradient direction correction, to obtain the brain mask b0 image.

[0049] 3) NODDI model analysis: The brain tissue mask file and the nii file were input into the NODDI model together to obtain the microstructural information of the whole brain (especially the white matter area), including Vic value, Viso value and ODI value. Subsequently, the NODDI image was extracted and analyzed using ITK-SNAP software. Since the white matter is easily affected by the ischemic load of carotid artery stenosis, special attention was paid to this area, and the white matter was divided into three areas: basal ganglia, corona radiata and centrum semiovale, and the Vic value, Viso value and ODI value of these three areas at different time periods were obtained.

[0050] 4) Comparative analysis: The above values ​​were used for comparative analysis to evaluate carotid artery stenosis and postoperative changes in brain structure.

[0051] 4. Statistical Analysis

[0052] Data were analyzed using SPSS 24.0 for Windows. GraphPad Prism version 8.0.0 for Windows, GraphPad Software, San Diego, California USA, www.graphpad.com, was used for graphics. Measurement data that conformed to normal distribution were expressed as mean ± standard deviation (SD); measurement data that did not conform to normal distribution were expressed as median (interquartile range); and count data were expressed as frequency (percentage). Two independent sample t-tests were used to compare measurement data and variance homogeneity between two groups that conformed to normal distribution; nonparametric Mann-Whitney U test was used to compare measurement data and variance homogeneity between two groups that did not conform to normal distribution. For paired design measurement data, paired t-tests were used for differences that conformed to normal distribution, and nonparametric Willcoxon rank sum test was used for differences that did not conform to normal distribution. Pearson chi-square test was used to count data in the four cell table groups. Pearson continuous calibrated chi-square test or Fisher's exact probability method was used for disqualified data. P < 0.05 was considered significant.

[0053] 5. Results;

[0054] The results of the comparison of NODDI parameters in the three brain regions of basal ganglia, corona radiata, and centrum semiovale between the surgical side and the contralateral side before CEA are shown in Table 1. The results of the comparison of NODDI parameters in the three brain regions of basal ganglia, corona radiata, and centrum semiovale between the surgical side before CEA and 3 days after CEA are shown in Table 2. The results of the comparison of NODDI parameters in the three brain regions of basal ganglia, corona radiata, and centrum semiovale between the surgical side before CEA and 3 months after CEA are shown in Table 3.

[0055] Table 1. Comparison of NODDI parameters between the surgical side and the contralateral side before CEA

[0056]

[0057] Table 2. Comparison of NODDI parameters on the surgical side before CEA and 3 days after surgery

[0058]

[0059]

[0060] Table 3. Comparison of NODDI parameters on the surgical side before and 3 months after CEA

[0061]

[0062] Cognitive function assessment: Each subject was scored with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) before and 3 months after CEA by a neurologist who was professionally trained in neuropsychological scoring. The total score of MMSE is 30 points, and a score <27 is considered cognitive impairment. The total score of MoCA is 30 points. If the years of education are less than or equal to 12 years, 1 point will be added, but the maximum score is still 30 points, and a score below 26 is considered cognitive impairment. The comparison of cognitive function before and 3 months after CEA is shown in Table 4.

[0063] Table 4. Comparison of cognitive function before and 3 months after CEA

[0064]

[0065]

[0066] *p<0.05, **p<0.01.

[0067] Conclusion: Before CEA, the Vic values ​​of the basal ganglia, corona radiata, and centrum semiovale on the surgical side were lower than those on the contralateral side (p<0.01), while the ODI value of the centrum semiovale was higher than that on the contralateral side (p<0.05). After CEA, with the recovery of carotid blood flow and the improvement of brain tissue perfusion, the blood flow velocity (MCV) of the middle cerebral artery on the surgical side increased, and the microstructural changes of the white matter appeared in a short period of time (3 days), which were manifested by the increase of Viso values ​​in the basal ganglia and centrum semiovale, and the decrease of ODI value in the corona radiata (p<0.05). As time passed to 3 months after surgery, the Vic values ​​of the basal ganglia and centrum semiovale increased, and the ODI value decreased, indicating that the microstructural changes of the brain tissue were gradually recovering. All patients had significant improvement in cognitive function 3 months after CEA treatment. These findings fully prove that the NODDI model can effectively evaluate the microstructural changes of brain tissue in patients with carotid artery stenosis. The NODDI model not only provides a noninvasive and effective means to evaluate the recovery of brain tissue perfusion and changes in cognitive function after CEA, but also reveals the short-term and long-term changes in white matter microstructure after CEA and the differences between different brain regions.

[0068] The above are only preferred embodiments of the present invention. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention. These should also be regarded as the protection scope of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A method for evaluating carotid artery stenosis and postoperative brain structural changes based on the NODDI model, characterized in that: The following steps are involved: Step S1, image data acquisition; Whole-brain DWI and NODDI scans were performed using a Philips 3.0T Ingenia MR scanner to obtain dicom sequences; Step S2, image data processing, specifically includes the following steps: Step 21, check the image quality parameters, pre-process the raw data that meet the requirements, and obtain the brain mask of the nii file and the b0 image; Step 22, input the brain tissue mask file and the nii file into the NODDI model to obtain the microstructure information of the whole brain, including Vic value, Viso value and ODI value; Step 23, using ITK-SNAP software to perform regional segmentation on the NODDI image, with special attention paid to the white matter area, and dividing it into three areas: basal ganglia, corona radiata, and centrum semiovale, and obtaining Vic values, Viso values, and ODI values ​​of the three areas at different time periods; Step 24: Perform comparative analysis using the values ​​obtained in step S23 to evaluate carotid artery stenosis and postoperative changes in brain structure.

2. The method for evaluating carotid artery stenosis and postoperative brain structural changes based on the NODDI model according to claim 1, characterized in that: In step S1, when a Philips 3.0T Ingenia MR scanner is used for scanning, the scanning coil is a 16-channel head coil, and a multi-slice single-shot spin echo planar pulse sequence along 32 isotropic diffusion gradient directions is used to perform whole-brain DWI and NODDI scanning.

3. The method for evaluating carotid artery stenosis and postoperative brain structural changes based on the NODDI model according to claim 1, characterized in that: In step 21, the specific operation of preprocessing is: using the mricron tool to convert the scanned DICOM sequence into a 4D Nifti format to obtain a nii file, and then using FDT in FSL to perform eddy current correction, including head motion eddy current and gradient direction correction, to obtain the brain mask of the b0 image from the processed data.

4. The method for evaluating carotid artery stenosis and postoperative brain structural changes based on the NODDI model according to claim 1, characterized in that: In step 23, the different time periods are before the operation, 3 days before the operation and 3 days after the operation, and 3 months before the operation and 3 months after the operation.

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