http://med-sci.cn/cgzcx/en/contents_list.asp?issue=202603 zh-cn An RSS feed for Chinese Journal of Magnetic Resonance Imaging http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.001 ObjectiveTo explore the abnormal changes in brain functional networks caused by patent foramen ovale (PFO) before the occurrence of structural lesions and the alterations in brain functional networks before and after PFO closure using functional magnetic resonance imaging (fMRI). Materials and MethodsThis study is a prospective study. Seventy-seven patients with PFO who were eligible for interventional occlusion in the Second Hospital of Hebei Medical University from October 2023 to October 2024 were selected and forty-two age- and gender-matched healthy controls (HC) were recruited from the community. The two groups are the PFO group and the HC group. All of them underwent fMRI examination. After six months of follow-up, twenty-four postoperative patients were collected and named as the postoperative group. The preoperative baseline data of these patients constituted the preoperative group. SPSS 25.0 software was used to analyze the gender, age and scale score data of the two groups. Using graph theory methods, based on the GRETNA software package in MATLAB R2013b, the whole-brain resting-state networks of two groups of patients were constructed and brain network analysis was performed. The results were analyzed by independent sample t test, and the preoperative and postoperative data were analyzed by paired sample t test. Independent component analysis (ICA) method was used to construct the resting-state networks (RSNs) of the whole brain and static functional network connectivity (sFNC) was used to evaluate the network connectivity strength of the whole brain. ResultsThere was no statistically significant difference in gender and age between PFO group and HC group (P > 0.05). The Montreal Cognitive Assessment (MoCA) score of the PFO group was lower than that of the HC group, and the Visual Symptoms and Quality of Life Questionnaire (VSQ) score was higher than that of the HC group. The differences were statistically significant (P < 0.05). The small worldness (σ) and normalized clustering coefficient (γ) of the small world network in the PFO group were higher than those in the HC group. Normalized characteristic path length (λ), clustering coefficient (Cp) and characteristic path length (Lp) were lower than those in the HC group. The differences were statistically significant (P < 0.05). The global efficiency (E_glob) of PFO group was higher than that of HC group. The differences were statistically significant (P < 0.05). There was no statistically significant difference in local efficiency (E_loc) between PFO group and HC group (P > 0.05). Compared with the HC group, the PFO group showed enhanced sFNC in left fronto-parietal networks (LFPN) and sensory-motor network (SMN), and LFPN and dorsal attention network (DAN) (P < 0.05). The sFNC between SMN and VN, DAN and SMN, DAN and default mode network (DMN) decreased (P < 0.05). There was no statistically significant difference in gender and age between the PFO group and the postoperative group (P > 0.05). There was no statistically significant difference in cognitive assessment between the preoperative group and the postoperative group (P > 0.05). The postoperative Visual Analogue Scale (VAS) score of the subjects was lower than the preoperative score and the postoperative VSQ score was also lower than the preoperative score. The differences were statistically significant (P < 0.05). There was no significant difference in the values of brain network properties between the preoperative group and the postoperative group (P > 0.05). The sFNC of DAN-VN increased after closure (P < 0.05). ConclusionsThe changes of brain network characteristic values in PFO patients indicate that the stability of brain network is impaired, and the dynamic regulation of brain networks tends to increase compensatory communication efficiency. The functional connectivity of brain network in PFO patients is changed, which may be related to cognitive, visual abnormalities or headache symptoms. The enhancement of sFNC in some brain networks after surgery may be related to the recovery of brain function. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.002 ObjectiveThis study combined magnetization prepared rapid gradient echo (MPRAGE) with resting-state functional magnetic resonance imaging (rs-fMRI) to investigate the differences in resting-state gray matter volume and functional connectivity (FC) between patients with type 2 diabetes mellitus (T2DM) and prediabetes mellitus (PreDM), as well as the correlation between statistically significant brain region imaging markers and neuropsychological scale scores. Materials and MethodsFrom July 2023 to December 2024,32 T2DM patients, 13 PreDM patients, and 30 individuals with normal glucose tolerance (NGT) were enrolled from the First Affiliated Hospital of Xinjiang Medical University. The gray matter volume differences among the three groups were analyzed, and brain regions with statistically significant gray matter volume differences were selected as seed points for FC analysis. The correlation between statistically significant brain regions in PreDM and T2DM patients and neuropsychological scale scores was also examined. Results(1) Gray matter volume [GRF (Gaussian random field correction), voxel level P < 0.001, cluster level P < 0.05]. Compared with the NGT group, the T2DM group showed reduced gray matter volume in the left middle temporal gyrus and left medial suprachiasmatic gyrus, while the PreDM group exhibited reduced gray matter volume in the left inferior frontal gyrus. Compared with PreDM, the T2DM group showed increased gray matter volume in the left inferior frontal gyrus. (2) FC (GRF, voxel level P < 0.01, cluster level P < 0.05). For the left middle temporal gyrus as the seed point, the FC analysis revealed statistically significant differences among the three groups in the right cerebellar area 6, right suboccipital gyrus, left middle frontal gyrus, left parietal gyrus, and left central anterior gyrus. Compared with the NGT group and PreDM group, the T2DM group showed increased FC values in these regions. Compared with the NGT group, the PreDM group exhibited decreased FC values in the right suboccipital gyrus. For the left medial suprachiasmatic gyrus as the seed point, the FC analysis revealed statistically significant differences among the three groups in the right precuneus and bilateral angular gyrus. Compared with the NGT group, the T2DM group showed increased FC values in the right precuneus. Compared with the PreDM group, the T2DM group exhibited increased FC values in both the right precuneus and bilateral angular gyrus. Compared with the NGT group, the PreDM group showed significantly decreased FC values in the bilateral angular gyrus. (3) Correlation analysis. The functional connectivity (FC) values between the left middle temporal gyrus and right suboccipital gyrus in the T2DM group showed a negative correlation with Montreal Cognitive Assessment (MoCA) scores (r = -0.393, P = 0.032). ConclusionsThis study revealed alterations in gray matter volume and FC values in specific brain regions among pre-diabetic (PreDM) and T2DM patients. Additionally, correlations were observed between FC values in certain brain regions and MoCA scores. T2DM patients may exhibit compensatory mechanisms, including increased FC values and reduced brain volume, as well as neurocompensatory mechanisms. These findings provide neuroimaging evidence for the early identification and intervention of cognitive impairment in diabetic patients. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.003 ObjectiveTo investigate the diagnostic value of diffusion weighted imaging (DWI)-based habitat imaging for the preoperative assessment of isocitrate dehydrogenase (IDH) mutation status in adult diffuse gliomas. Materials and MethodsA total of 99 adult patients with diffuse gliomas (73 IDH wildtype and 26 IDH mutant) were retrospectively enrolled. Based on the intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) models, the perfusion fraction (f), true diffusion coefficient (D), and mean kurtosis (MK) parameters were calculated. K-means clustering was applied to voxel-wise data within the tumor volume of interest (VOI) to construct habitat maps, and the volumetric fraction of each habitat was quantified. Logistic regression was used to develop diffusion parameter model, habitat model, age model and integrated model. The performance of different models was evaluated using five-fold cross-validation. DeLong test was employed to compare the performance of the integrated model with that of the other models. ResultsThe proportion of Habitat 1 (Hypercellular hypoperfusion hyperheterogeneous habitat) was higher in IDH wild-type gliomas compared with IDH mutant gliomas (0.56 ± 0.25 vs. 0.30 ± 0.20, P < 0.001), whereas the proportion of Habitat 2 (Hypocellular hypoperfusion hypoheterogeneous habitat) was lower (0.39 ± 0.25 vs. 0.64 ± 0.21, P < 0.001). DeLong test showed that the integrated model achieved the highest diagnostic performance [AUC = 0.902, 95% confidence interval (CI): 0.759 to 1.000]. Shapley additive explanations analysis indicated that age contributed most to model predictions, followed by the proportion of Habitat 1. ConclusionsHabitat imaging based on IVIM and DKI parameters effectively reflects the intratumoral heterogeneity of gliomas. When combined with clinical characteristics, it enables accurate, noninvasive prediction of IDH mutation status, offering a promising imaging biomarker for preoperative molecular subtyping of gliomas. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.004 ObjectiveTo investigate the value of quantitative parameters derived from intravoxel incoherent motion (IVIM) imaging in evaluating the therapeutic effects of bone marrow mesenchymal stem cell (BMMSC) in a rat model of peripheral nerve injury. Materials and MethodsThirty-six Sprague-Dawley (SD) rats were randomly assigned to a BMMSC group or a phosphate-buffered saline (PBS) control group. Each group underwent magnetic resonance imaging, motor function assessment, and histological analysis at five time points: before the operation and 1, 2, 3, and 4 weeks after the operation. IVIM perfusion fraction (f) assessed nerve perfusion, while T2-weighted imaging (T2WI) and T2 mapping assessed edema. Toluidine blue staining and immunofluorescence were used to assess neural structural alterations. The sciatic function index (SFI) was employed to assess functional recovery. The expression of vascular endothelial cell-specific markers (CD31) and inflammatory factors (IL-1α, IL-10 and PPARγ) was evaluated by immunohistochemistry. ResultsIVIM-f recovery, myelin regeneration, and nerve fiber repair were all significantly better in the BMMSC group than in the control group (all P < 0.05). Immunohistochemical analysis showed lower IL-1α and higher IL-10 and PPARγ levels in the BMMSC group than in the control group (all P < 0.05). And the expression of CD31 was significantly increased in the BMMSC group (P < 0.05). Moreover, the IVIM-f was closely correlated with both histopathological and functional outcomes. ConclusionsQuantitative IVIM parameters, particularly IVIM-f, can reflect perfusion recovery and nerve regeneration earlier than T2 values after stem cell therapy in rats with peripheral nerve injury, and may serve as a potential imaging biomarker for evaluating the therapeutic efficacy of stem cell treatment. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.005 ObjectiveTo investigate the value of a multi-sequence MRI-based radiomics model in differentiating pulmonary adenocarcinoma from inflammatory pulmonary masses. Materials and MethodsA retrospective analysis was conducted on 136 patients with pathologically confirmed lung adenocarcinoma and inflammatory pulmonary masses, whose clinical and MRI imaging data were collected for evaluation. All patients were randomly divided into a training set (n = 96) and a test set (n = 40) in a 7∶3 ratio. Through univariate and multivariate logistic regression analyses, indicators with discriminatory significance (P < 0.05) in the clinical and MRI features of patients with lung adenocarcinoma and inflammatory lung occupying lesions were screened. Tumor regions of interest (ROI) were delineated on four sequence images: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Radiomics features were then extracted using PyRadiomics. Highly correlated redundant features were removed based on the Spearman correlation coefficient. Subsequently, the remaining features were screened using ten-fold cross-validation and the least absolute shrinkage and selection operator (LASSO). Logistic regression (LR) was then applied to construct single-sequence and combined multi-sequence radiomics models. Model performance was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), based on which the optimal radiomics model was selected. Finally, clinical, clinical-imaging, and clinical-imaging-radiomics combined model were developed by integrating clinical features, MRI features, and the optimal radiomics model, and a visual nomogram was constructed. Statistical comparison of AUCs was performed using the DeLong test, and the clinical utility of the models was assessed via decision curve analysis (DCA). ResultsAge, smoking history, straightening sign, ADC value, and time-intensity curve (TIC) type were identified as independent predictors for pulmonary adenocarcinoma (P < 0.05). Among the radiomics models, the multi-sequence MRI (T1WI+T2WI+ADC+DCE-MRI) model achieved the best diagnostic performance, with AUCs of 0.888 and 0.738 in the training and test sets, respectively. Further integration of clinical, MRI, and radiomics features yielded a combined model, analysis of which showed that the combined model had higher predictive performance (P < 0.05), with AUCs reaching 0.924 and 0.853 in the training and test sets, respectively. ConclusionsThe combined model based on clinical, MRI features, and multi-sequence MRI radiomics shows good diagnostic efficacy in differentiating pulmonary adenocarcinoma from inflammatory pulmonary masses. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.006 ObjectiveTo explore the predictive value of intravoxel incoherent motion (IVIM) histogram parameters combined with clinical features for pathologic complete response after neoadjuvant therapy in patients with locally advanced rectal cancer. Materials and MethodsClinical and imaging data of 197 patients (training set: 130, validation set: 67) with rectal cancer who underwent neoadjuvant therapy and radical surgery in Sichuan Cancer Hospital from January 2019 to December 2025 were retrospectively analyzed. Patients were categorized into significantly response and non-significantly response groups based on postoperative pathological findings. The mean (Mean), median (Median), standard deviation (SD), maximum (Maximum), and variance (Variance) of the histogram parameters of true diffusion coefficient (D), pseudo diffusion coefficient (D^*), and perfusion fraction (f) were obtained based on IVIM images. Multivariate logistic regression analyses was used to establish combined parameters model. The area under curve (AUC) of receiver operating characteristic, sensitivity, accuracy, and specificity were used to assess the diagnostic performance. DeLong test was used for comparisons of the AUCs among independent risk predictors and combined parameters model. ResultsThe values of f_Mean, f_Median, f_SD, f_Maximum, f_Variance, D^*_Mean, D^*_Median, D^*_SD, and D^*_Variance were significantly higher in the significantly response group compared with the non-significantly response group in the training set (all P < 0.05). No significant difference were found in the D values between the two groups (all P > 0.05). The AUC of combined parameters model combing four independent risk factors was 0.894 [95% confidence interval (CI): 0.827 to 0.941] and 0.831 (95% CI: 0.714 to 0.928) in the training set and validation set, respectively, which was significantly higher than any other independent risk factors according to DeLong test (all P < 0.05). The accuracy, sensitivity and specificity of the combined parameters model was 0.854 (95% CI: 0.781 to 0.909), 0.711 (95% CI: 0.541 to 0.845), 0.913 (95% CI: 0.835 to 0.961) and 0.806 (95% CI: 0.701 to 0.896), 0.731 (95% CI: 0.522 to 0.884), 0.854 (95% CI: 0.708 to 0.944) in the training set and validation set, respectively. ConclusionsIVIM histogram parameters can provide some basis for predicting the efficacy of neoadjuvant therapy in patients with rectal cancer, and the diagnostic efficacy of the model can be improved after further combining clinical features. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.007 ObjectiveTo investigate the value of deep learning (DL) models based on multiparametric MRI, including diffusion-weighted imaging (DWI), fat-suppressed T2-weighted imaging (T2-FS), and T1-weighted contrast-enhanced imaging (T1CE), in preoperatively predicting tumor deposit (TD) status in rectal cancer. Materials andMethodsA retrospective analysis was conducted on 321 patients from two centers who underwent total mesorectal excision and were pathologically diagnosed with rectal adenocarcinoma. Patients were divided into a TD-positive group (n = 81) and a TD-negative group (n = 240) based on pathology. Patients from center 1 (n = 273) were randomly split 8∶2 into a training set and a test set, while patients from center 2 (n = 48) served as an external validation set. Using the ResNet18 DL network, four models were built: a DWI-DL model, a T2-FS-DL model, a T1CE-DL model, and a combined-DL model. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the predictive performance of each DL model. ResultsIn the single-sequence models, the T1CE-DL model achieved AUCs of 0.842 (95% CI: 0.808 to 0.891), 0.792 (95% CI: 0.752 to 0.828), and 0.747 (95% CI: 0.700 to 0.777) in the training, test, and external validation sets, respectively, demonstrating superior predictive performance compared to the T2-FS-DL and DWI-DL models. The T2-FS-DL model yielded AUCs of 0.805 (95% CI: 0.774 to 0.843), 0.766 (95% CI: 0.724 to 0.801), and 0.725 (95% CI: 0.690 to 0.767) in the three datasets, respectively. For the DWI-DL model, the AUCs were 0.801 (95% CI: 0.753 to 0.832), 0.745 (95% CI: 0.703 to 0.775), and 0.747 (95% CI: 0.702 to 0.779), respectively. The combined-DL model achieved the highest AUCs, reaching 0.909 (95% CI: 0.877 to 0.956), 0.875 (95% CI: 0.834 to 0.919), and 0.816 (95% CI: 0.767 to 0.852) in the training test, and external validation sets, respectively. Its diagnostic performance was significantly superior to that of the three single-sequence models (DeLong test, all P < 0.05). ConclusionsThe combined-DL model shows good predictive value and generalization ability for preoperatively assessing TD status in rectal cancer patients. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.008 ObjectiveThis study employed a meta-analysis approach to systematically evaluate the diagnostic performance of diffusion-weighted imaging (DWI) in staging bladder cancer, specifically in differentiating T1 or lower stages from T2 or higher stages. Materials and MethodsA comprehensive literature search was conducted in CNKI, PubMed, and Embase databases (from the establishment to March 2025) using inclusion criteria. The quality assessment of diagnostic accuracy studies-2 (OUADAS-2) was used to assess the quality of the included studies. Heterogeneity testing was performed using RevMan 5.3 and Stata 18.0 statistical software. Meta-analysis of diagnostic sensitivity and specificity was conducted, with subsequent construction of summary receiver operating characteristic (SROC) curves and calculation of evaluation metrics including area under the curve (AUC). ResultsTwelve studies were included in the analysis. The pooled sensitivity of DWI for T-staging of bladder cancer was 0.89 [95% (confidence interval, CI): 0.78 to 0.95], specificity was 0.85 (95% CI: 0.77 to 0.90), AUC reached 0.92 (95% CI: 0.89 to 0.94), positive likelihood ratio (PLR) was 5.9 (95% CI: 3.9 to 8.8), negative likelihood ratio (NLR) was 0.13 (95% CI: 0.06 to 0.27), and diagnostic odds ratios (DOR) was 45 (95% CI: 19 to 109). Deek^,s funnel plot was basically symmetrical, and the slope coefficient was not statistically significant (P = 0.44), suggesting that there was no significant publication bias in the studies included inour analysis. ConclusionsDWI demonstrates high diagnostic value in T-staging of bladder cancer and provides crucial reference information for clinical treatment decision-making. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.009 ObjectiveTo investigate the value of the effective transverse relaxation rate (R2^*) derived from blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) in assessing the response to neoadjuvant chemotherapy (NACT) and prognostic risk factors in cervical squamous cell carcinoma. Materials and MethodsThis study retrospectively enrolled 64 patients with cervical squamous cell carcinoma who underwent surgery after NACT at our institution. All patients underwent BOLD-MRI before and after NACT. Tumor histopathological features, including lymph node metastasis (LNM), lymphovascular space invasion (LVSI), and depth of stromal invasion, were recorded based on postoperative pathology. According to Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), patients were classified into a partial response (PR) group (n = 54) and a stable disease (SD) group (n = 10). Inter-observer agreement was assessed using the intra-class correlation coefficient (ICC). The Mann-Whitney U test or independent samples t-test was used to compare R2^* values between groups. To evaluate the predictive value and correlation of R2^* parameters with tumor histopathological features and response to NACT, binary logistic regression and Pearson or Spearman correlation analysis were employed, respectively. Furthermore, receiver operating characteristic (ROC) curves were plotted to assess the predictive performance of R2^* values, and internal validation was performed using the bootstrap method. ResultsBoth post-NACT R2^* (R2^*_post) and the change in R2^* (ΔR2^*) demonstrated predictive value for LVSI, LNM, and depth of stromal invasion (all P < 0.05). Area under the ROC curve (AUC) analysis indicated that R2^*_post exhibited superior predictive performance compared to ΔR2^*for LVSI, stromal invasion depth, and prognostic risk factors (adjusted AUCs: 0.862 vs. 0.792, 0.742 vs. 0.687, and 0.749 vs. 0.699, respectively), with optimal cutoff values of 21.44 Hz, 21.68 Hz, and 21.68 Hz, respectively. In contrast, for LNM, ΔR2^*showed better predictive efficacy than R2^*_post (adjusted AUC: 0.792 vs. 0.738), with an optimal cutoff value of 1.145 Hz. Multivariate analysis further confirmed that R2^*_post was an independent predictor of LVSI, and ΔR2^* was an independent predictor of LNM. ConclusionsR2^*_post and ΔR2^* may serve as potential imaging biomarkers for assessing treatment response to NACT and prognostic risk factors in cervical squamous cell carcinoma. Lower values of these parameters are associated with unfavorable clinical outcomes. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.010 ObjectiveTo investigate the spatiotemporal progression of white matter injury following germinal matrix hemorrhage (GMH) using multimodal magnetic resonance imaging (MRI), and to explore the neuroinflammatory response and motor function outcomes. Materials and MethodsA total of 38 postnatal day 5 Sprague-Dawley rats were randomly assigned to either a GMH group (induced by collagenase injection) or a sham-operated control group. Longitudinal MRI scans, including T2-weighted imaging (T2WI), susceptibility-weighted imaging (SWI), and diffusion tensor imaging (DTI), were performed at 12 hours, and 1, 3, 5, and 30 days post-surgery. Quantitative analysis of DTI parameters, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), was conducted in 7 regions of interest (ROI): the striatum, hippocampus, internal capsule, external capsule, corpus callosum, motor cortex, and somatosensory cortex. Acute neuroinflammation was assessed via Western blot (WB), quantitative real-time polymerase chain reaction (qRT-PCR), and immunohistochemistry (IHC). Long-term motor function was evaluated using the pole test. ResultsDTI analysis revealed significantly decreased FA values accompanied by increased MD and RD values in multiple brain regions in the GMH group, indicating compromised white matter integrity. FA values in the striatum declined significantly from 1 day post-GMH onward (P < 0.001), and this decrease was sustained through 30 days (P = 0.004), along with elevated MD and RD values (P < 0.05). In the hippocampus, FA values began to decline at 3 days post-GMH compared with controls (P < 0.05) and remained lower until 30 days (P < 0.05), with concurrent elevations in MD, AD, and RD (P < 0.05). By 30 days post-GMH, FA values in all seven ROIs on the injured side were significantly lower than those on the contralateral side (P < 0.05), indicating clear lateralization of white matter injury. Molecular analyses showed a significant upregulation of pro-inflammatory cytokines (IL-6, IL-1β, TNF-α; P < 0.001) and marked activation of both microglia and astrocytes during the acute phase post-GMH. Behavioral testing showed a significantly prolonged descent time in the pole test for the GMH group (P < 0.05), demonstrating a correlation between white matter structural damage and motor dysfunction. ConclusionsDTI sensitively captures dynamic microstructural alterations in white matter following GMH. These structural alterations are significantly associated with neuroinflammatory responses and long-term motor deficits. Our findings provide crucial imaging evidence for understanding the underlying mechanisms of GMH pathology. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.011 ObjectiveTraditional imaging techniques provide limited diagnostic information regarding microcirculatory impairment and inflammatory burden in acute pancreatitis (AP). This study aimed to establish a multi-parametric magnetic resonance imaging (MRI)-based approach to characterize pancreatic injury in a rat model of AP. The predictive performance of contrast enhancement ratio (CR) and histogram parameters (Skewness and Kurtosis) was evaluated. In addition, we assessed whether these imaging biomarkers could sensitively capture the therapeutic effects of emodin in vivo. Materials and MethodsForty-five Sprague-Dawley (SD) rats were randomly assigned to the control (CN) group, an AP group and an emodin-treated (ET) group (n = 15 per group). Using 3.0 T MRI to obtain enhanced T1WI images, and measuring the contrast ratio (CR), Skewness and Kurtosis of the images. At the same time, detecting the levels of serum amylase, lipase and creatinine, conducting histological scoring (HS), and determining the expression of inducible nitric oxide synthase (iNOS). Spearman rank correlation and multivariate regression analyses were conducted to determine the associations between imaging biomarkers and pathological criteria. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the imaging-based predictive model for AP severity. ResultsCompared with CN group, AP rats exhibited distinct MRI features characterized by an increase of CR and Skewness (both P < 0.001), along with decreased Kurtosis (P < 0.05). The three imaging indicators in the ET group were all improved compared with those in the AP group (P < 0.05), and the differences were statistically significant, approaching the levels of the CN group. Correlation analysis demonstrated that CR was significantly and moderately to highly positively correlated with the positive expression levels of HS and iNOS (ρ = 0.64 to 0.85, P < 0.001), while Kurtosis was significantly negatively correlated with HS (ρ = -0.84, P < 0.001). Multivariate regression analysis demonstrated that CR was independently associated with HS and iNOS expression (β = 6.69, P < 0.05), whereas Kurtosis was negatively associated with HS (β = -2.56, P < 0.001). The combined CR and Kurtosis model achieved an AUC of 0.95 for predicting severe AP (HS ≥ 12), with an overall accuracy of 89.6%. CR alone yielded an AUC of 0.98 for predicting high iNOS expression. ConclusionsCR, Skewness and Kurtosis enable non-invasive quantification of AP-related alterations in perfusion status and tissue heterogeneity. CR primarily reflects perfusion abnormalities, while Kurtosis more accurately indicates tissue heterogeneity. Emodin mitigates pancreatic injury by reducing inflammatory burden and improving microcirculatory perfusion. This multi-parametric MRI workflow provides a reliable imaging-based strategy for grading AP severity and monitoring therapeutic response. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.012 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.013 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.014 ^, sexual health. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.015 ^,s disease, and research has gradually expanded to other fields, with growing evidence indicating that GS dysfunction is involved in the pathophysiological processes of various psychiatric disorders. However, research on GS in psychiatric disorders is still in its early stages, and some diseases remain underexplored in this field. This review systematically outlines the structure, function, and influencing factors of the GS, summarizes current imaging techniques used to assess GS function, discusses the limitations of existing research, and provides future perspectives. It aims to offer new insights into the intrinsic relationship between GS function and psychiatric disorders, and to serve as a reference for further research in this area. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.016 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.017 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.018 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.019 ^, experience and subjective rating scales, which hinders the advancement of personalized precision medicine. With the rapid development of magnetic resonance imaging (MRI), therapeutic outcomes can now be objectively evaluated using diverse imaging biomarkers, including brain structural volume, cortical thickness, white matter integrity, functional connectivity, and neurometabolite levels. However, previous studies have largely focused on unimodal approaches. Complicated by the high clinical heterogeneity of the disease, the predictive efficacy and reproducibility of various biomarkers remain controversial, making it difficult to directly guide personalized treatment decisions. This review systematically synthesizes the research progress of multi-modal MRI technologies in predicting treatment efficacy for MDD. It specifically addresses current research status, methodological limitations, and the impact of clinical heterogeneity. The aim is to provide theoretical insights and future directions for establishing robust imaging biomarkers to guide personalized treatment strategies for MDD. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.020 ^, functional recovery and quality of life. Current clinical assessment of PSD primarily relies on depression rating scales and medical history. However, these methods are limited by inherent subjectivity and challenges in early identification, and objective neuroimaging biomarkers remain scarce. Recent advancements in multimodal MRI have provided novel tools for the systematic characterization of brain structural, functional, and microstructural alterations associated with PSD. Nevertheless, existing findings are fragmented, and the interplay between different imaging modalities and their clinical translational value has not been systematically synthesized. By reviewing the research progress of structural MRI, functional MRI, diffusion MRI, and magnetic resonance spectroscopy (MRS) in PSD, this article highlights the potential of cross-scale multimodal integration in elucidating neural mechanisms and improving risk prediction. Furthermore, the current limitations and future directions of the field are analyzed. This review aims to provide a neuroimaging framework for objective assessment, biomarker exploration, and the development of precision intervention strategies for PSD. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.021 ^,s disease (PD) is an age-related neurodegenerative disorder; early diagnosis and treatment are critical to slowing its progression. Although its prodromal stage offers a critical intervention window, conventional MRI lacks specificity, and no imaging gold standard exists for diagnosis. Accurate identification of prodromal PD is essential for precise clinical management, with multimodal imaging features playing a key role in its detection and personalized treatment. This paper examines the clinicopathological features of prodromal PD, analyzes current diagnostic frameworks and risk assessment strategies, describes multimodal imaging characteristics, and reviews artificial intelligence (AI) applications in its diagnosis and prognostic prediction. It also identifies limitations of current research and proposes future directions, to provide radiologists with imaging references for enhancing early diagnosis, treatment optimization and personalized risk assessment of prodromal PD. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.022 ^,s disease (PD) is one of the most prevalent neurodegenerative disorders, primarily characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. Early diagnosis of PD is crucial for timely therapeutic intervention and improved clinical outcomes. MRI, as a non-invasive imaging modality, has shown great promise in the early detection of PD, particularly in evaluating structural and functional alterations in the substantia nigra. This review highlights four representative MRI techniques used to assess the substantia nigra in early-stage PD: Nigrosome imaging, neuromelanin-sensitive MRI, quantitative susceptibility mapping, and diffusion tensor imaging. We systematically discuss the imaging principles, recent research advancements, and the diagnostic performance and limitations of each technique in detecting early PD. Moreover, we explore the utility of these imaging approaches in the differential diagnosis of atypical Parkinsonian syndromes. Finally, we outline potential future research directions and clinical applications, aiming to provide theoretical foundations and technical support for advancing the early diagnosis of PD. ]]> Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.023 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.024 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.025 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.026 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.027 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.028 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.029 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.030 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.031 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.032 Fri,20 Mar 2026 00:00:00 GMT http://med-sci.cn/cgzcx/en/en_articlexml.asp?doi=10.12015/issn.1674-8034.2026.03.033 Fri,20 Mar 2026 00:00:00 GMT