Iconography detection method for overactive bladder (OAB) patient
By using MRI and statistical analysis, the PVS index of OAB patients was quantified, revealing the association between PVS abnormalities and OAB symptoms, providing personalized treatment plans, filling the research gap on central nervous system dysfunction in OAB patients, and realizing effective assessment and treatment monitoring of OAB symptoms.
Patent Information
- Application Number
- CN202511079190.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-02
- Publication Date
- 2025-11-14
AI Technical Summary
Current technologies have not studied the perivascular space (PVS) in the brain of patients with overactive bladder (OAB), and there is a lack of discussion on the correlation between its abnormalities and symptoms, which affects the understanding of central nervous system function.
Brain structural imaging data were acquired using MRI technology. White matter regions and subcortical nuclei were segmented using Freesurfer software. PVS-enhanced images were processed using linear transformation and Frangi filter to calculate PVS indices. Statistical analysis was performed using SPSS software to determine PVS abnormalities in OAB patients and to develop personalized treatment plans.
This study revealed a positive correlation between abnormal PVS and OAB symptoms, providing a new pathological association and a new perspective for the study of OAB pathogenesis. Furthermore, it demonstrated how PVS indicators can be used to develop personalized treatment plans and monitor treatment effectiveness.
Smart Images
Figure CN120954677A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of imaging technology, specifically relating to an imaging detection method for patients with overactive bladder (OAB). Background Technology
[0002] The role of the central nervous system in the pathophysiological mechanism of OAB has received widespread attention. Functional imaging studies have found that OAB patients have functional abnormalities in several key brain regions, including the prefrontal cortex, pancreatic islets, anterior cingulate cortex, and periaqueductal gray matter [8]. Diffusion tensor imaging studies have found that abnormalities in brain white matter microstructure may be related to central nervous system dysfunction in OAB patients. These studies suggest that abnormalities in brain structure or function are likely related to central nervous system dysfunction in OAB patients.
[0003] Perivascular space (PVS) is the space surrounding blood vessels in the brain, closely related to the drainage of cerebral fluid and the clearance of metabolic waste. Abnormalities in PVS may reflect abnormalities in cerebral fluid circulation and metabolism, thereby affecting the function of the central nervous system. However, PVS has never been studied in patients with oral cerebral abscess (OAB). This study aims to investigate abnormalities in PVS indicators in OAB patients and their correlation with symptoms, hoping to provide a new perspective for the study of the pathogenesis of OAB. Summary of the Invention
[0004] The purpose of this invention is to provide an imaging method for patients with overactive bladder (OAB), comprising the following steps: 1) Use MRI technology to acquire brain structural imaging data and convert it into NIFTI format; 2) The brain images were segmented using Freesurfer software to obtain masks of white matter regions and subcortical nuclei.
[0005] 3) Register the T2-weighted image onto the T1-weighted image using a linear transformation, and calculate the PVS augmented image.
[0006] 4) Apply the Frangi filter to process the PVS augmented image to estimate the vascular characteristic measure of each voxel, thereby achieving PVS segmentation; 5) Calculate the volume and quantity of PVS in white matter regions and subcortical nuclei regions to form a PVS index. 6) Based on the comparison of the PVS index with a pre-set threshold, determine whether the subject has OAB.
[0007] Furthermore, the PVS index includes the number of white matter PVS, the volume of white matter PVS, the number of subcortical PVS, and the volume of subcortical PVS.
[0008] Furthermore, SPSS software was used for statistical analysis to compare the differences in PVS indicators between OAB patients and healthy controls. Spearman correlation coefficient analysis was used to analyze the correlation between white matter PVS quantity and OAB symptom score. Multiple linear regression analysis was performed to explore the effects of white matter PVS quantity, age, gender, education level and disease duration on OAB symptom score. Partial correlation analysis was used to control for the effect of disease duration on the relationship between white matter PVS quantity and OAB symptom score.
[0009] Furthermore, the OAB symptom score mentioned above includes urinary urgency score, OAB severity score, and symptom distress score.
[0010] Furthermore, based on the evaluation results of the PVS index, the PVS index of OAB patients is obtained through the above-mentioned imaging detection methods and statistical analysis methods. According to the abnormality of the PVS index, a personalized treatment plan is formulated, including but not limited to intervention measures targeting the central nervous system. The changes of the patient's PVS index during the treatment process are monitored to evaluate the treatment effect.
[0011] Furthermore, abnormalities in the number of white matter PVS were used as a biological indicator of OAB severity.
[0012] Furthermore, the study aims to identify abnormally increased numbers of PVS in the brains of subjects; regulate PVS-related brain fluid circulation and metabolic waste clearance functions using drugs or other interventions; and monitor changes in OAB symptom scores to assess treatment effectiveness.
[0013] Furthermore, the quantification step of the PVS image further includes: performing a positive correlation analysis on the number of PVS in the white matter region with the urinary urgency index, OABSS score, and other OAB-related clinical scores.
[0014] The beneficial effects of this invention are: This study is the first to reveal the pathological association between abnormalities in the perivascular space (PVS), a marker of the lymphoid system, and overactive bladder (OAB). In subjects excluding those with organic brain lesions, OAB patients showed a significant increase in PVS volume in the white matter region, which was positively correlated with clinical symptoms such as the urinary urgency index and OABSS score (p<0.05). This groundbreaking finding introduces lymphoid system dysfunction into the study of the central mechanisms of OAB. Attached Figure Description
[0015] Figure 1 For the T1 image of s0001; Figure 2 This is the T2 image of s0001; Figure 3 For s0001, green represents white matter and red represents subcortical nuclei; Figure 4 For s0001 PVS, only white matter; Figure 5 For s0001, PVS is only subcortical; Figure 6 The image shows the PVS of s0001, with green representing white matter and red representing subcortical nuclei in a 3D view.
[0016] Figure 7 PVS for s0001, white matter only, 3D view; Figure 8 PVS for s0001, subcortical only, 3D view. Detailed Implementation
[0017] The present invention will be further described below with reference to the embodiments and accompanying drawings. Example 1
[0018] All participants were recruited from the outpatient department of the Central Hospital Affiliated to Jiangnan University between May 2024 and February 2025. Prior to enrollment, all subjects were independently diagnosed by two experienced urologists according to the "Guidelines for the Diagnosis and Treatment of Urological and Andrological Diseases in China" (2022 edition). Healthy individuals matched for age, sex, and education level with the OAB group were carefully selected from the hospital's physical examination center as the control group. Before enrollment, all participants underwent a detailed medical history review to confirm their eligibility for inclusion. Exclusion criteria for both groups included breastfeeding and pregnant women; those with urogenital diseases or a history of urogenital surgery; those with a history of specific medication use, such as receiving anti-anxiety or antidepressant medications within the past 3 months; those with a family history of specific hereditary diseases, such as Alzheimer's disease; and those with significant abnormalities in brain anatomy on T1-weighted imaging, such as cerebral infarction or other vascular injuries.
[0019] 2.3 Scale Assessment On the day of enrollment, all OAB patients underwent the Overactive Bladder Symptom Score (OABSS) and the Quality of Life Assessment in Bladder Disease (QAB) by a specialist to assess the severity of their disease. The Urgency Rating Scale was used to assess the degree of urinary urgency (0 points for no urgency, 1 point for being able to hold urine for more than 1 hour when urge arises, 2 points for being able to hold urine for 10-60 minutes when urge arises, 3 points for being able to hold urine for less than 10 minutes when urge arises, and 4 points for needing to urinate when urge arises). The Montreal Cognitive Assessment Scale (MoCA) was used to assess cognitive function, and relevant clinical information was recorded, including gender, age, height, weight, education level, duration of disease, and underlying medical history.
[0020] 2.4 MRI examination All subjects' MRI images were acquired at the Department of Radiology, Affiliated Central Hospital of Jiangnan University, using a Siemens Magnetom Vida 3.0T MRI scanner (flexible 32-channel coil for the skull). All subjects emptied their bladders before the MRI scan and were instructed to remain awake, quiet, and still before the test. Sponge head pads and earplugs were used to stabilize their heads and reduce noise.
[0021] A rapid three-dimensional localization imaging sequence was used to scan the intermediate layers in the axial, coronal, and sagittal planes. The parameters were as follows: TR (Repetition Time) = 8.6ms, TE (Echo Time) = 4.0ms, flip angle = 20°, slice thickness = 7mm, acquisition matrix = 256×256, and FOV (Field of view) = 250mm×250mm.
[0022] T1-weighted image data of the subjects were acquired using a three-dimensional magnetized fast gradient echo (3D-MPRAGE) sequence with the following parameters: TR=1900ms, TE=2.48ms, flip angle=9°, slice thickness=1mm, number of slices=176, acquisition matrix=256×256, FOV=250mm×250mm.
[0023] T2-weighted image data of the subjects were acquired using a Turbo Spin Echo (TSE) sequence. Axial scanning was performed with AC-PC as the reference plane, covering the entire brain. Scanning parameters: TR=3200 ms, TE=408ms, flip angle=T2 var, number of slices=192, slice thickness=0.9mm, interslice spacing=0 mm, matrix=256×256, FOV=230 mm×230 mm, scan time=4 minutes 05 seconds.
[0024] 2.5 Data Preprocessing and PVS Index Calculation The data was converted to NIFTI format using MRIcroGL for subsequent processing and analysis. Preprocessing was performed using the Freesurfer, FSL, and QIT (https: / / cabeen.io / qitwiki / ) software packages, with the following steps: 1) Freesurfer segmentation: The structural image data of the subjects was segmented using Freesurfer to obtain white matter masks and subcortical nucleus masks. 2) PVS segmentation: First, the T2w image was registered to the T1w image using a linear transformation. The T1w image was then divided by the T2 image to obtain the PVS-enhanced image (EPC, Enhanced PVS Contrast). The Frangi filter was applied to the EPC image for PVS segmentation. Specifically, when calculating the PVS map, the Frangi filter was applied to the PVS-enhanced image to estimate the vascular characteristic measure of each voxel feature vector in the image's Hessian matrix. This operation was implemented using the QIT (https: / / cabeen.io / qitwiki / ) toolkit. The Frangi filter used default parameters (…). = 0.5, = 0.5). However, the parameter The value was set to half of the maximum Hessian norm. The Frangi filter estimated vascular property metrics at different scales from 0.1 to 5 voxels, providing the maximum probability for calculating PVS maps. Finally, the volume and number of PVS in white matter regions and subcortical nucleus regions were calculated.
[0025] 1.6 Statistical Analysis Statistical analysis was performed using SPSS software (version 25.0.1.0). Non-continuous variables, such as gender, were analyzed using the chi-square test between the overactive bladder group and the healthy control group; continuous variables, such as education level scores and age, were analyzed using the two independent samples t-test. A p-value < 0.05 was considered statistically significant. Variables following a normal distribution were expressed as mean ± standard deviation; those not following a normal distribution or with unequal variances were expressed as median (interquartile range). Statistical analysis of MRI data between the two groups was conducted with gender, age, and years of education as covariates; a p-value < 0.05 was considered statistically significant. Spearman's correlation coefficient was used to analyze the correlation between the differential indicators and clinical symptom scores. Furthermore, multiple linear regression analysis was performed to explore the effects of age, gender, education level, and disease duration on clinical scores. Partial correlation analysis was used to clarify the specific association between the differential indicators and clinical symptoms; a p-value < 0.05 was considered statistically significant.
[0026] 1. Results 1.1 General Information of the Subjects This study included 116 patients (39 males and 77 females) with treatment-resistant OAB and 112 healthy controls (35 males and 77 females). There were no statistically significant differences between the two groups in terms of gender ratio, age, height, weight, education level score, and Moca score, indicating good comparability between the two groups.
[0027] In this study, the scores of the OAB group on the Urgency Rating Scale, OABSS, OAB-Q1, and OAB-Q2 were significantly higher than those of the healthy control group (Table 1).
[0028] Table 1 General Information Table: Table 1. Basic information of participants
[0029] Abbreviations: OABSS: Overactive Bladder Symptom Score; OAB-Q-1: Overactive Bladder Questionnaire Short Form 1; OAB-Q-2: Overactive Bladder Questionnaire Short Form 2.
[0030] 1.2 The number of white matter PVS in patients with OAB was significantly different from that in the control group. The number of white matter perivascular spaces (PVS) in patients with ophthalmic angina (OAB) was significantly lower than that in the healthy control group (HC) (Figure 2). Specifically, the mean value of the number of white matter perivascular spaces (WMPVS) in the OAB group was 325.53 ± 82.931, while that in the HC group was 303.52 ± 75.988 (p = 0.013). It is noteworthy that the other three PVS indicators—white matter perivascular space (WMPVS) volume, number of white matter perivascular spaces (WMPVS), subcortical PVS volume, and subcortical PVS clusters—did not show significant differences between the OAB and control groups. However, the mean values in the OAB group were all higher than those in the control group. These results suggest that OAB patients may have abnormal expansion of the PVS in the white matter fiber bundles of the brain.
[0031] 1.3 Correlation between the number of white matter perivascular spaces (pvs) and clinical characteristics of patients with ophthalmic angina (OAB) The figure summarizes the correlation between the number of white matter perivascular spaces (pvs) and various clinical characteristics in patients with oral inflammatory bowel disease (OAB). Spearman correlation coefficients showed a significant positive correlation between the number of white matter perivascular spaces and the OABSS score (OAB severity; r = 0.229, p = 0.014), OAB-Q1 score (symptom distress; r = 0.234, p = 0.011), and urinary urgency score (r = 0.321, p < 0.001), indicating that a higher number of white matter perivascular spaces correlates with more severe symptoms such as urinary frequency and urgency. Furthermore, the number of white matter perivascular spaces was also positively correlated with age (r = 0.421, p < 0.001), suggesting that the number of white matter perivascular spaces increases with age. It is noteworthy that the number of white matter perivascular spaces was not significantly correlated with education level, body mass index (BMI), disease duration, or OAB-Q2 (health-related quality of life) score.
[0032] 1.4 Correlation between the number of white matter perivascular spaces (pvs) and other demographic characteristics and symptom scores We analyzed all observed clinical features, including the number of white matter perivascular spaces, using linear correlation analysis. We found that these symptom scores were also influenced by age, sex, education level, and disease duration (Table 2). To address potential confounding factors and to precisely examine the association between the number of white matter perivascular spaces and OAB symptoms, we performed multiple linear regression analysis. In this analysis, we treated all significantly different symptom scores (including urinary urgency score, OABSS, and OABQ1) as dependent variables, while age, sex, education level, disease duration, and the number of white matter perivascular spaces were treated as independent variables. Multiple linear regression analysis revealed a more explicit association. The number of white matter perivascular spaces affected the symptoms of OAB patients (urinary urgency score, OABSS, and OABQ1), and a longer disease duration affected OABSS and OABQ1, meaning that a longer disease duration resulted in more severe symptoms and a poorer quality of life. Age, sex, and education level had no significant effect on any OAB symptom score.
[0033] Table 2 Correlation analysis of clinical characteristics of OAB patients
[0034] Abbreviations: OABSS: Overactive Bladder Symptom Score; OAB-Q-1: Overactive Bladder Questionnaire Short Form 1; OAB-Q-2: Overactive Bladder Questionnaire Short Form 2.
[0035] 1.5 Correlation between the number of white matter perivascular spaces (pvs) and clinical symptoms in patients with ovascular abscess (OAB) Considering the interaction effect of disease duration, we performed partial correlation analysis. This analysis showed that, after controlling for disease duration, the number of white matter perivascular spaces (PVS) remained significantly correlated with OABSS and OABQ1 (see Table 3).
[0036] Table 3 Spearman correlation and partial correlation analysis of wm_clusters values and symptom scores in OAB patients (controlling for age, sex, and disease duration).
[0037] Abbreviations: OABSS: Overactive Bladder Symptom Score; OAB-Q-1: Overactive Bladder Questionnaire Short Form 1; OAB-Q-2: Overactive Bladder Questionnaire Short Form 2 discuss: This study is the first to reveal the pathological association between abnormalities in the perivascular space (PVS), a marker of the lymphoid system, and overactive bladder (OAB). In subjects excluding those with organic brain lesions, OAB patients showed a significant increase in PVS volume in the white matter region, which was positively correlated with clinical symptoms such as the urinary urgency index and OABSS score (p<0.05). This groundbreaking finding introduces lymphoid system dysfunction into the study of the central mechanisms of OAB.
[0038] Recent neuroimaging evidence suggests that oral bladder abscess (OAB) is associated with central nervous system regulatory abnormalities, but the potential role of the lymphoid system remains unexplored. This study innovatively employs an automated PVS quantification method to calculate the volume and number of PVS in the white matter and subcortical regions. This study is the first to reveal characteristic PVS abnormalities in OAB patients: ① The number of white matter perivascular spaces (WM-PVS) is significantly higher than in healthy controls; ② The number of WM-PVS is significantly positively correlated with clinical scores such as the urgency index, OABSS, and OAB-Q1. We propose a dual mechanism of action: ① Structural damage—PVS expansion may affect the signal transmission efficiency of the spinothalamic-prefrontal pathway by altering the microenvironment surrounding nerve fibers; ② Neurometabolic imbalance—impaired clearance function of the lymphoid system may trigger a neuroinflammatory cascade, leading to abnormal excitability of the micturition control network. The positive correlation between the number of WM-PVS and OAB clinical symptoms further supports the potential link between PVS abnormalities and OAB symptoms.
[0039] It is noteworthy that PVS abnormalities are currently considered a characteristic marker of cerebral small vessel disease (CSVD) and are known to be associated with cognitive impairment [literature]. However, in this study, there was no significant difference in MoCA scores for cognitive function between the two groups (p=), nor was there a significant difference in the incidence of hypertension and diabetes between the two groups, suggesting that PVS burden may be an independent central mechanism of OAB. This finding provides a new biological perspective on the "brain-bladder axis" theory and suggests that PVS burden may serve as an imaging biomarker for the severity of OAB. This study opens up new directions for research on the comorbidity mechanisms of OAB and CSVD and may drive innovation in treatment strategies—interventions targeting CSVD (such as blood-brain barrier function regulation and neuroinflammatory modulation) may become a new direction for OAB management.
Claims
1. An imaging method for detecting overactive bladder (OAB) in patients, characterized in that, Includes the following steps: MRI technology was used to acquire structural brain images of the subjects, including T1-weighted and T2-weighted images, and then converted them into NIFTI format; The Freesurfer software was used to automatically segment brain images and obtain three-dimensional masks of white matter regions and subcortical nuclei. The T2-weighted image is registered onto the T1-weighted image by linear transformation, and the ratio of the T1-weighted image to the T2-weighted image is calculated to generate an enhanced image of the perivascular space. The Frangi filter is applied to perform vascular characteristic analysis on the enhanced image of the perivascular space. By calculating the vascular characteristic measure of each voxel feature vector of the Hessian matrix of the image, the automatic segmentation of the perivascular space is achieved. The volume and number of perivascular spaces in the white matter region and the subcortical nucleus region were calculated separately to form a quantitative index of perivascular spaces; Based on the quantitative indicators of the perivascular space and the pre-established discrimination threshold, it is determined whether the subject has OAB.
2. The detection method according to claim 1, characterized in that, The quantitative indicators of perivascular spaces include: the number of perivascular spaces in the white matter region, the volume of perivascular spaces in the white matter region, the number of perivascular spaces in the subcortical region, and the volume of perivascular spaces in the subcortical region.
3. The detection method according to claim 1 or 2, characterized in that, The parameters of the Frangi filter are set as follows: α=0.5, β=0.5, and parameter c is set to half of the maximum Hessian norm. The filter estimates vascular characteristic metrics at different scales from 0.1 to 5 voxels.
4. The detection method according to claim 1, characterized in that, It also includes statistical analysis steps: The SPSS software was used to compare the differences in perivascular space parameters between OAB patients and healthy controls. Spearman correlation coefficient analysis was used to analyze the correlation between the number of perivascular spaces in the white matter region and OAB symptom scores; Multiple linear regression analysis was conducted to explore the combined effects of the number of perivascular spaces in the white matter region, age, gender, education level, and disease duration on OAB symptom scores. Partial correlation analysis was used to control for confounding factors such as disease duration on the relationship between the number of perivascular spaces and OAB symptom scores.
5. The detection method according to claim 1, characterized in that, The number of perivascular spaces in the white matter region is used as an objective biological marker of OAB severity for disease grading and prognostic assessment.
6. The detection method according to claim 1, characterized in that, Further steps include treatment monitoring: Identify the distribution of regions with an abnormally increased number of perivascular spaces in the brain of the subjects; Modify the cerebral fluid circulation and metabolic waste clearance function related to the perivascular space using drugs or other interventions; Regularly monitor changes in OAB symptom scores to quantitatively assess treatment effectiveness.
7. The detection method according to claim 1, characterized in that, The quantitative analysis of the perivascular space images further includes: performing a positive correlation verification analysis on the number of perivascular spaces in the white matter region and clinical indicators such as the urinary urgency index, OABSS score, and OAB-Q1 score.
8. The detection method according to claim 1, characterized in that, The MRI scan parameters are set as follows: T1 weighted images were generated using a 3D-MPRAGE sequence: TR=1900ms, TE=2.48ms, flip angle=9°, slice thickness=1mm, number of slices=176, acquisition matrix=256×256; T2-weighted imaging uses a fast spin echo sequence: TR=3200ms, TE=408ms, slice thickness=0.9mm, number of slices=192, acquisition matrix=256×256, to ensure high-quality imaging data of the perivascular space.
9. The detection method according to claim 1, characterized in that, The pre-established discrimination threshold is determined in the following way: Collect perivascular space data from a large sample of OAB patients and healthy controls; ROC curve analysis was used to determine the optimal diagnostic cut-off point; Verify the diagnostic accuracy, sensitivity, and specificity of this threshold in independent samples.
10. An overactive bladder diagnostic system based on the method of any one of claims 1-9, comprising: MRI data acquisition module, used to acquire images of brain structures; The image preprocessing module is used for format conversion and image registration; Automatic segmentation module for perivascular spaces, integrating Freesurfer and Frangi filtering algorithms; The quantitative analysis module calculates relevant indicators of the perivascular space. The diagnostic judgment module performs OAB diagnosis based on preset thresholds. The report generation module outputs standardized diagnostic reports.