Objective To propose an unsupervised few-shot PET partial volume correction method based on an improved deep image prior. Methods Propose a novel PET partial volume correction method by enhancing the classical deep image prior framework. Specifically, population information is used for pre-training; the conventional loss function is replaced with a back-projection fidelity term combined with regularization by denoising; neural blind deconvolution is introduced to estimate the system point spread function; MRI anatomical features are fused via an attention mechanism to further improve correction performance. Results Validation experiments were carried out on PET-SORTEO simulation dataset and ADNI clinical dataset. The test results of quantitative indicators showed that the PSNR, SSIM, CNR, CLIP and NIQE of the proposed method achieved superior performance over the compared approaches. Conclusion The proposed method, built upon the modified deep image prior for PET partial volume correction, effectively enhances the image quality of PET images.

Objective To investigate the value of radiomics models based on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences combined with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score in the diagnosis of prostate cancer (PCa) in the prostate-specific antigen (PSA) gray zone (4-10 ng/mL). Methods A retrospective analysis was conducted on the clinical and imaging data of 119 patients with PSA in the gray zone admitted to the First Affiliated Hospital of Bengbu Medical University from January 2020 to September 2025. All patients underwent pathological biopsy, including 35 cases of PCa and 84 cases of benign prostatic hyperplasia (BPH), and all received multiparametric magnetic resonance imaging (mpMRI) prior to surgery. The patients were randomly divided into a training set (95 cases, 28 PCa and 67 BPH) and a test set (24 cases, 7 PCa and 17 BPH) at an 8∶2 ratio. Regions of interest (ROIs) were manually delineated on T2WI and DWI images. After feature extraction, Min-max normalization was applied, and Pearson correlation along with the Relief algorithm were used for feature dimensionality reduction and selection. The synthetic minority over-sampling technique (SMOTE) was used to balance the data. In the training set, T2WI- and DWI-based radiomics models were built using support vector machine (SVM) and naive Bayes classifiers with 5-fold cross-validation. The features from the two sequences were fused to construct a combined radiomics model. Independent clinical predictors, including the PI-RADS v2.1 score, were screened by logistic regression to build a clinical model, which was ultimately integrated into a combined model. The diagnostic performance was assessed by ROC curve, nomogram, and decision curve analysis. Results In the test set, the AUC of the T2WI and DWI radiomics models were 0.790 and 0.807, with accuracies of 0.792 and 0.667, respectively.The biparametric MRI-based fusion radiomics model showed better diagnostic performance, with an AUC of 0.852 (95% CI: 0.775-0.910), but the difference was not statistically significant (P>0.05). When the fusion model was further integrated with the PI-RADS v2.1 score, the combined model achieved an AUC of 0.870, significantly outperforming the PI-RADS v2.1 score alone (0.781, P<0.01). Conclusion Radiomics features derived from bpMRI can effectively quantify tumor heterogeneity.Their integration with the PI-RADS v2.1 score may provide more reliable decision support for the accurate and non-invasive diagnosis of prostate cancer in the PSA gray zone, potentially reducing unnecessary biopsies.

Objective To investigate the feasibility and value of dual-echo ultrashort echo time (Dual-echo UTE) magnetic resonance imaging in quantitatively assessing condylar cortical bone abnormalities in patients with temporomandibular disorders (TMD), and to compare differences in cortical bone parameters among the normal control (NC) group, temporomandibular disorders without osteoarthritis (TMDwoOA) group, and temporomandibular joint osteoarthritis (TMJOA) group. Methods A total of 74 TMD patients (62 TMJOA joints, 63 TMDwoOA joints) were recruited from the Department of Radiology, Hainan Hospital of PLA General Hospital from March 2023 to January 2025, alongside 30 healthy volunteers (83 NC joints). All participants were performed with bilateral routine MRI and closed-mouth dual-echo UTE MRI scans of the temporomandibular joints. Following group assignment based on condylar morphology, ImageJ software was employed to analyze UTE images to determine the cortical bone area and porosity index (PI). To validate the measurement accuracy of the dual-echo UTE sequence, CT images from 12 TMD patients and 7 volunteers were included as the gold standard, and intra-group correlation coefficient was used to analyze the consistency of UTE measurements of condylar structural parameters. Results UTE sequences presented strong agreement with CT in condylar bone measurements (P<0.001). Cortical bone area in the TMJOA group was significantly reduced compared to the NC group (122.05 vs 131.06 mm2, P<0.05). The mean PI presented a progressive increase with TMD severity (NC: 28.05% vs TMDwoOA: 31.00% vs TMJOA: 34.00%, P<0.05). Conclusion Dual-echo UTE sequences could enable precise quantification of condylar cortical bone abnormalities in TMD patients. The study confirms that TMJOA progression is accompanied by a reduction in cortical bone area and an increase in porosity, providing a reliable imaging tool for monitoring bone degeneration in clinical diagnosis and efficacy evaluation.

Objective To investigate the correlation between serum nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4) levels and left ventricular geometric patterns and functional parameters in patients with primary hypertension (PHT). Methods A total of 338 patients diagnosed with PHT at the First Hospital of Shanxi Medical University from June 2024 to May 2025 were enrolled. Based on left ventricular mass index (LVMI) and relative wall thickness (RWT), patients were categorized into four groups: normal geometry (NG, n=209), concentric remodeling (CR, n=55), eccentric hypertrophy (EH, n=50), and concentric hypertrophy (CH, n=24). Serum NOX4 levels were measured using enzyme-linked immunosorbent assay in all groups. General clinical characteristics, biochemical parameters, and left ventricular structural and functional indices were compared among the four groups. Pearson correlation analysis was performed to evaluate the associations between serum NOX4 levels and left ventricular structural/functional parameters. Multivariate logistic regression was used to identify risk factors for abnormal left ventricular geometry in PHT patients. Results Serum NOX4 levels exhibited a stepwise increase across the severity gradient of left ventricular remodeling, with the highest levels in the CH group, followed by EH, CR, and NG groups, and the CH group showed significantly higher levels than the NG group (P<0.05). Significant differences were observed among the four groups in BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), neck circumference, waist circumference, hip circumference, left ventricular end?diastolic diameter (LVEDd), interventricular septal thickness (IVST), left ventricular posterior wall thickness (LVPWT), LVMI, RWT, left atrial diameter (LAD), left atrial volume index (LAVI), deceleration time (DT), isovolumic relaxation time (IVRT), early (E) and late (A) transmitral flow velocities, E/A ratio, early diastolic mitral annular velocity (e′), E/e′ ratio, left ventricular ejection fraction (LVEF), left ventricular fractional shortening (LVFS), and global longitudinal strain (GLS) (all P<0.05). Pearson correlation analysis revealed that serum NOX4 levels were positively correlated with IVST (r=0.403, P<0.001), LVPWT (r=0.611, P<0.001), LAD (r=0.207, P<0.001), and E/e′ (r=0.636, P<0.001), and negatively correlated with LVEF (r=-0.271, P<0.001), E (r=-0.206, P<0.001), e′ (r=-0.800, P<0.001), E/A (r=-0.178, P=0.001), and GLS (r=-0.658, P<0.001). Multivariate logistic regression analysis demonstrated that NOX4 was independently associated with EH (OR=2.437, P=0.038) and CH (OR=4.156, P=0.008). Conclusion Elevated serum NOX4 levels are closely associated with abnormal left ventricular geometry in patients with PHT. The graded changes in NOX4 concentrations suggest that NOX4 may be involved in the pathological process of left ventricular remodeling in this population.

Objective To investigate the associations of left atrial (LA) epicardial adipose tissue volume (EATV) and CT attenuation with left atrial volume (LAV) in atrial fibrillation (AF). Methods This retrospective study enrolled 77 patients with AF and 77 patients with sinus rhythm who underwent cardiac CT angiography from January 2022 to June 2025. Total EATV (T-EATV), EAT CT attenuation, LA-EATV, and LA-EAT CT attenuation were measured using Siemens syngo.via. Three-dimensional LA models were reconstructed using 3D Slicer to calculate LAV. Between-group differences were compared, and multivariable logistic regression and Spearman correlation analyses were performed. Results Heart rate and body mass index were significantly higher in the AF group than in the sinus rhythm group (P<0.05). Age, gender, hypertension, diabetes mellitus, and coronary artery disease did not differ significantly between groups (P>0.05). LAV, T-EATV, LA-EATV, T-EAT CT attenuation, LA-EAT CT attenuation, and pericoronary fat CT attenuation values of all three coronary arteries were significantly higher in the AF group (P<0.001). ROC curve analysis showed that LAV had the highest diagnostic performance for AF, with an AUC of 0.849, an optimal cutoff of 117.45 cm3, a sensitivity of 76.6%, and a specificity of 84.4%. LA-EATV (OR=1.337, 95% CI: 1.051-1.701, P=0.018), LAV (OR=1.045, 95% CI: 1.018-1.073, P=0.001), LCX pericoronary fat CT attenuation (OR=1.132, 95% CI: 1.016-1.261, P=0.025), and NT-proBNP (OR=1.004, 95% CI: 1.001-1.007, P=0.019) were independently associated with AF. T-EATV and T-EAT CT attenuation were not independently associated with AF. LAV was moderately positively correlated with LA-EATV (r=0.392, P<0.001). Conclusion LA-EATV and LAV were independently associated with AF and showed a moderate positive correlation. LA-EATV may provide complementary information to LAV for assessing AF-related left atrial remodeling from the perspective of local fat deposition.

Objective To investigate the diagnostic value of a combined model based on multimodal ultrasound features, including conventional ultrasound (US), ultramicroangiography (UMA), strain elastography (SE), and contrast-enhanced ultrasound (CEUS), for differentiating mass-type plasma cell mastitis (PCM) from invasive ductal carcinoma (IDC). Methods A retrospective analysis was performed on 231 patients, including 74 patients with mass-type PCM and 157 patients with IDC, pathologically confirmed at Inner Mongolia Autonomous Region People's Hospital from September 2024 to May 2026. All patients underwent US, UMA, SE, and CEUS examinations. Variables with P<0.05 in univariate analysis were entered into multivariate logistic regression analysis to identify independent predictors, and a combined model was constructed and presented as a nomogram. ROC curves were used to evaluate the diagnostic performance of the combined model, and the differences in AUCs between the combined model and individual modalities were compared using the DeLong test. Internal validation was performed using 1000 bootstrap resamples. Results Age, vertical orientation, posterior acoustic attenuation, calcifications, high stiffness on SE, increased vessel tortuosity (VT), shortened arrival time (AT), and increased ascending slope (AS) were identified as independent predictors of IDC (P<0.05). The combined model achieved an AUC of 0.946 (95% CI: 0.918~0.973), which was higher than those of US (0.811), SE (0.708), UMA (0.732), and CEUS (0.784) (DeLong test, P<0.001). At the optimal cutoff value, the sensitivity and specificity were 76.4% and 94.6%, respectively. The corrected AUC after bootstrap internal validation was 0.951. The calibration curve demonstrated good model fit (Hosmer-Lemeshow test, P=0.721). Conclusion The combined model based on multimodal ultrasound features significantly improves the diagnostic performance for differentiating mass-type PCM from IDC and provides quantitative support for noninvasive diagnosis and individualized clinical decision-making.

Objective To investigate the diagnostic value of transvaginal three-dimensional ultrasound parameters combined with serum tumor necrosis factor-α (TNF-α) and interleukin-17A (IL-17A) on the severity of intrauterine adhesions (IUA). Methods A total of 123 patients with suspected IUA who were treated in the hospital were selected from March 2024 to March 2026. All patients underwent transvaginal three-dimensional ultrasound examination and serum TNF-α and IL-17A detection. With hysteroscopy and American Fertility Society (AFS) score as the gold standard, the patients were divided into mild adhesion group (n=48), moderate adhesion group (n=41) and severe adhesion group (n=34). The differences in trnasvaginal three-dimensional ultrasound parameters [endometrial thickness (ED), endometrial volume (EV), vascularization flow index (VFI)] and serum TNF-α and IL-17 A levels were compared among the three groups. Pearson correlation was used to analyze the correlation between transvaginal three-dimensional ultrasound parameters, serum TNF-α and IL-17A and AFS score. Multivariate logistic regression model was utilized to analyze the influencing factors for severe IUA. ROC curve was drawn to analyze the efficiency of transvaginal three-dimensional ultrasound parameters, serum TNF-α and IL-17A alone and in combination in the diagnosis of severe IUA. Results The ED, EV and VFI were ranked from highest to lowest as follows: mild adhesion group, moderate adhesion group and severe adhesion group (P<0.05); Serum TNF-α and IL-17A levels in the three groups showed the opposite ranking (P<0.05). Pearson correlation analysis showed that ED, EV and VFI were all negatively correlated with AFS score (r=-0.589, -0.645, -0.621) while serum TNF-α and IL-17A were positively associated with AFS score (r=0.634, 0.618, P<0.05). Multivariate logistic analysis showed that high levels of TNF-α (OR=3.232) and IL-17A (OR=2.798) were risk factors for severe IUA, while high levels of ED (OR=0.269), EV (OR=0.205) and VFI (OR=0.240) were protective factors (P<0.05). ROC curve showed that the AUCs for diagnosing severe IUA by ED, EV, VFI, TNF-α and IL-17A were 0.786, 0.791, 0.772, 0.781 and 0.775 respectively, and the highest AUC (0.897) was observed when all five indicators were combined (P<0.05). Conclusion Transvaginal three-dimensional ultrasound parameters and serum TNF-α and IL-17A levels are closely related to the severity of IUA, and the combined detection of the above indicators can significantly improve the diagnostic efficiency on severe IUA.

Objective To investigate the feasibility of pre-dual-flow injection bolus tracking protocol with free-breathing for dual-source CT spectral pulmonary angiography (CTPA) and assess its image quality. Methods A retrospective analysis was conducted on 100 patients who underwent CTPA at Nanfang Hospital of Southern Medical University from March to December 2025. The patients were divided into pre-dual-flow injection bolus tracking protocol (group A, n=50) or test bolus protocol (group B, n=50). The CT values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and radiation dose were compared between the two groups in objective analysis. The vascular filling, arteriovenous separation, beam hardening artifact of SVC and respiratory artefact were scored in subjective analysis. Results Compared with Group B, the CT values were significantly higher in the right pulmonary basal artery trunk, left lower artery, right posterior basal (A₁₀), and left posterior basal (A₁₀)_{in group A, while the CT value of the superior vena cava (SVC) was significantly lower in group A (P<0.05). The SNR and CNR were significantly higher in the left lower artery, right posterior basal (A10) and left posterior basal (A10) in group A (P<0.05). The beam hardening artefact of SVC was significantly lower in group A (P<0.05). Conclusion The pre-dual-flow injection bolus tracking protocol with free-breathing and combined with lower volume of contrast medium has significant advantages in reducing beam hardening artefact of SVC and ensuring image quality for dual-source CTPA.}

Objective To construct and validate six prediction models based on preoperative enhanced CT images in the arterial and portal venous phases, compare their predictive performance, and identify the optimal model for evaluating treatment response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). Methods A total of 175 HCC patients who underwent TACE were included. Among them, 113 patients from the public WAW-TACE dataset were assigned as the training set, while 62 patients retrospectively collected at the Affiliated Hospital of Qiqihar Medical University from July 2020 to October 2025 served as the external validation set. Regions of interest (ROIs) were manually delineated on arterial phase and portal venous phase images using 3D Slicer software, from which radiomics and deep learning features were subsequently extracted. After feature selection was performed using univariate analysis, correlation analysis, and the least absolute shrinkage and selection operator (LASSO), prediction models were constructed using the support vector machine (SVM) algorithm. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity, and was validated in the external validation set. Results Feature extraction was performed separately on arterial and portal phase images, yielding 1,130 radiomics features and 512 deep learning features from each phase. After feature selection and dimensionality reduction, the arterial phase combined model performed the best comprehensively, with an AUC of 0.851 (95% CI: 0.778-0.924) in the training set and 0.808 (95% CI: 0.700-0.917) in the external validation set, and provided superior clinical net benefit compared with the single model and the portal venous phase combined model. Conclusion The combined model constructed based on the arterial phase can predict treatment response after TACE in patients with HCC to a certain extent, providing auxiliary reference for clinical treatment decision-making and prognostic assessment.

Objective To develop and validate a 2.5D deep learning (DL) model integrated with the ResNet101 architecture based on multimodal MRI for the preoperative prediction of cytokeratin-19 (CK-19) expression in hepatocellular carcinoma (HCC). Methods A total of 218 patients diagnosed with HCC who underwent preoperative MRI at the Third Affiliated Hospital of Soochow University from January 2018 to January 2025 were retrospectively enrolled, and randomly divided into a training cohort (TC, n=152) and a validation cohort (VC, n=66) at a ratio of 7:3. CK-19 expression was determined as either positive or negative according to immunohistochemical analysis. A 2.5D DL model was constructed on the ResNet101 framework, and quantitative imaging features were extracted from T2-weighted imaging (T2WI), arterial phase (AP), and portal venous phase (PVP) MRI sequences. The LASSO algorithm was applied to screen the most robust and discriminative imaging features, which were subsequently incorporated into a 2.5D DL model with an Extreme Gradient Boosting classifier. The predictive performance of each model for CK-19 expression was assessed using the AUC. Calibration curves and decision curve analysis (DCA) were utilized to evaluate the predictive consistency and clinical net benefit of the established models. Gradient-weighted Class Activation Mapping (Grad-CAM) was performed to visualize the intuitive heat maps within HCC lesions. Results For the single-sequence models, the T2WI-based 2.5D model achieved AUC values of 0.81 (95% CI: 0.74-0.88) in the TC and 0.71 (95% CI: 0.54-0.86) in the VC. The AP-based 2.5D model yielded AUCs of 0.83 (95% CI: 0.76-0.89) and 0.74 (95% CI: 0.69-0.84) in the TC and VC, respectively. The PVP-based 2.5D model exhibited AUC values of 0.80 (95% CI: 0.72-0.88) for the TC and 0.70 (95% CI: 0.57-0.88) for the VC. Notably, the multimodal combined 2.5D MRI model obtained the optimal predictive performance, with AUCs of 0.87 (95% CI: 0.80-0.93) in the TC and 0.78 (95% CI: 0.64-0.93) in the VC, accompanied by favorable calibration and superior clinical net benefit. Grad-CAM visualization successfully localized the focal regions corresponding to CK-19 positive expression in HCC tissues. Conclusion The ResNet101-based 2.5D model derived from multimodal MRI enables effective prediction of CK-19 expression in HCC. In particular, the integrated multimodal MRI model achieves high predictive stability and favorable clinical net benefit.

Objective To investigate the value of the atherogenic index of plasma (AIP) combined with coronary CT angiography (CCTA)-derived parameters, including the pericoronary adipose tissue attenuation index (FAI) and CT-derived fractional flow reserve (CT-FFR), for assessing severe coronary artery stenosis. Methods This retrospective study included 168 patients with coronary heart disease treated at the First Affiliated Hospital of Bengbu Medical University between January and December 2025. According to the maximum coronary artery stenosis severity, patients were classified into a mild-to-moderate stenosis group (n=68) and a severe stenosis group (n=100). Clinical characteristics and laboratory findings were collected. AIP was calculated, and CCTA-derived parameters, including FAI and CT-FFR, were obtained. Correlation analysis, multivariable logistic regression, ROC curve analysis, and the DeLong test were performed to evaluate the discriminatory performance of individual indicators and combined models for severe coronary artery stenosis. Results The severe stenosis group had higher triglyceride levels, AIP, and FAI values and a lower CT-FFR than the mild-to-moderate stenosis group (P<0.05). AIP was positively correlated with FAI (r=0.377) and negatively correlated with CT-FFR (r=-0.208). AIP and FAI were positively correlated with stenosis severity, whereas CT-FFR was negatively correlated with stenosis severity (P<0.05). Multivariable logistic regression showed that AIP (OR=1.468, 95% CI: 1.248-1.727) and FAI (OR=1.075, 95% CI: 1.022-1.130) were independent risk factors for severe coronary artery stenosis, whereas CT-FFR was a protective factor (OR=0.501, 95% CI: 0.358-0.700, P<0.05). The AUCs of AIP, FAI, and CT-FFR alone were 0.786, 0.734, and 0.732, respectively. The combined AIP + FAI + CT-FFR model achieved the highest AUC (0.850, 95% CI: 0.793-0.907), with a sensitivity of 75.0% and a specificity of 82.4%, outperforming the individual indicators and the two-parameter imaging model (P<0.05). Conclusion AIP, FAI, and CT-FFR are closely associated with the severity of coronary artery stenosis. Their combination improves the discrimination of severe coronary artery stenosis.

Objective To investigate the image quality of three-dimensional zero echo time sequence of 3T magnetic resonance imaging in lung magnetic resonance imaging. Methods This study included 78 patients with pulmonary magnetic resonance examination from June 2021 to September 2025 at Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, and 27 patients with pulmonary magnetic resonance examination were included from March 2024 to January 2026 at the First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine. Scanning sequences included 3D ZTE and 3D LAVA FLEX. The image quality of lung tissues obtained by ZTE sequence and LAVA FLEX sequence was scored by two diagnostic physicians, and the difference between the two sequences was compared.Then, the regions of interest (ROI) of the two sequences were delineated by two imaging technicians, and the lung parenchyma signal difference ratio, signal-to-noise ratio and contrast-to-noise ratio of the two sequences at the levels of lung apex, tracheal bifurcation and four-chamber heart were calculated respectively,the differences of the signal difference ratio and the signal-to-noise ratio of the trachea and bronchus in the lung parenchyma of each segment of the two sequences were compared. Results Compared with the LAVA FLEX sequence, the ZTE sequence can obtain higher lung parenchyma and lung bronchial image signal contrast.The signal difference ratio and signal-to-noise ratio of ZTE sequence were significantly higher than those of LAVA FLEX sequence, and the Kappa values were 0.761 and 0.660 (P<0.01). Conclusion In the imaging of lung parenchyma, trachea and bronchus, the image quality of ZTE sequence is good, and the fine anatomical structure of lung can be clearly displayed.The sequence supports free breathing scanning without patient 's breath-holding cooperation, which can reduce respiratory motion artifacts.Compared with conventional lung MRI sequences, ZTE sequence has outstanding imaging advantages and can be used as an important imaging sequence for lung MRI evaluation.

Objective To analyze the risk factors for secondary lung injury in acute pancreatitis (AP) based on CT imaging findings, and to construct a predictive model for secondary lung injury in patients with AP. Methods A total of 411 patients with AP treated at Fifth affiliated Hospital of Southern Medical University from January 2018 to December 2025 were enrolled in this study. Patients were divided into a lung injury group (n=320) and a non-lung injury group (n=91) according to the presence or absence of secondary lung injury. Binary logistic regression analysis was employed to identify the independent risk factors for secondary lung injury in AP patients, and a predictive model was subsequently constructed based on these risk factors. Results Binary logistic regression analysis identified the following as independent risk factors for secondary lung injury in AP patients: moderate-to-severe pancreatitis severity, APACHE II score≥8, BISAP score≥3, pleural effusion, and perirenal fat space involvement (all P<0.05). A predictive model was constructed based on these risk factors. ROC curve analysis demonstrated that the AUC for predicting secondary lung injury was 0.933 (95%CI: 0.908-0.958) in the training cohort and 0.947 (95%CI: 0.903-0.991) in the validation cohort. The Hosmer-Lemeshow goodness-of-fit test yielded P values of 0.971 and 0.157 for the training and validation cohorts, respectively, indicating satisfactory model calibration. Decision curve analysis revealed that the model provided a large net benefit across a wide range of threshold probabilities, demonstrating favorable clinical utility. Conclusion Secondary lung injury in AP patients is closely associated with pancreatitis severity, APACHE II score, BISAP score, pleural effusion, and perirenal fat space involvement. The CT imaging-based predictive model constructed in this study exhibits high predictive value for secondary lung injury in patients with AP.

Objective To explore the application value of MRI multimodal imaging in the clinical diagnosis of neonatal bilirubin encephalopathy (BE). Methods 34 cases of BE newborns treated in our hospital from June 2021 to August 2025 were collected. According to serum bilirubin levels, they were divided into mild group (n=18), moderate group (n=12), and severe group (n=4). All patients underwent brain MR plain scan examination, including conventional T2W1, T1W1, T2 weighted imaging liquid attenuation reversal recovery sequence (T2 FLAIR) sequence, diffusion-weighted imaging (DWI), and magnetic sensitivity weighted imaging (SWI) sequence. Compare the signal characteristics of different MRI sequences in each group, including the signal intensity of globus pallidus T2W1 and T1W1, apparent diffusion coefficient (ADC) value, and SWI examination results. Results The probability of high signal intensity on T1WI (50.00%) and T2 FLAIR (38.89%) in the mild group was lower than that in the moderate group (91.67%, 83.33%) and severe group (100.00%, P<0.05). The T1WI signal intensity of bilateral globus pallidus in the mild group was lower than that in the moderate and severe groups (P<0.05), and the moderate group was lower than the severe group (P<0.05). There was no statistically significant difference in T2WI signal intensity between the three groups of bilateral pale globus (P>0.05). The ADC value in the mild group was higher than that in the moderate and severe groups (P<0.05), and the moderate group was higher than the severe group (P<0.05). The detection rate of SWI microbleeds showed that the mild group (33.33%) was lower than the moderate group (91.67%) and severe group (100.00%) (P<0.05). Conclusion MRI multimodal imaging technology can effectively distinguish neonatal BE of different severity levels, providing important basis for clinical diagnosis and treatment.

Objective To introduce a standardized workflow for multitrait-multimethod (MTMM) analysis using the R package lavaan and demonstrate its application in the assessment of scale construct validity. Methods Using lavaan and empirical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), cognitive assessment data from participants aged≥65 years were analyzed. Three measurement methods (ECOG, MMSE, and MoCA) and four trait dimensions (memory, language, visuospatial ability, and attention) were included. The procedures for MTMM model specification, parameter estimation, validity assessment, and goodness-of-fit evaluation in lavaan were systematically illustrated. Results The correlated trait-correlated method (CTCM) model showed substantially better overall fit than the correlated trait-correlated uniqueness (CTCU) model (CTCM: CFI=0.988, TLI=0.975, RMSEA=0.040, SRMR=0.028; CTCU: CFI=0.922, TLI=0.828, RMSEA=0.106, SRMR=0.103). However, significant method effects and incomplete separation of some trait factors indicated that convergent and discriminant validity remained suboptimal. Conclusion The R package lavaan enables efficient and standardized MTMM model analysis. Its open-source availability, clear syntax, and streamlined analytical workflow make it a practical tool for construct validity assessment in clinical and psychological research.

Objective To apply optimization of lean processes at the entire workflow of contrast-enhanced CT (CECT) examinations in the radiology department to achieve higher efficiency and productivity. Methods A retrospective study was conducted on 860 patients who underwent CECT scan at the Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, during two periods: February-April 2025 (pre-optimization of lean process) and September-November 2025 (post-optimization process). The pre-optimization process control group enrolled 404 patients (55% male), and the experimental group enrolled 456 patients (48% male). The inspection average waiting time, examination on-time rate, and incidence of iodinated contrast extravasation adverse events were compared between the two groups. Results The inspection average waiting time decreased from 59.46 ± 24.00 min to 19.29 ± 14.66 min after lean process optimization (P<0.001); the on-time rate for examinations improved from 39.4% (159/404) to 51.1% (233/456) (P<0.001); the incidence of iodinated contrast agent extravasation decreased from 3.7% (15/404) to 1.8% (8/456) (P=0.04); and patient satisfaction rose from 50.4% to 74.2% (P<0.01). Conclusion The optimization of the entire process of CECT in the radiology department based on lean management can significantly improve examination efficiency, enhance the quality of medical services.

Pancreatic cancer is associated with an extremely poor prognosis. Conventional therapies, including surgery and chemotherapy, face major limitations such as restricted indications, severe toxic side effects, and insufficient targeting capability. Owing to their high targeting efficiency and favorable biocompatibility, nanomaterials have emerged as a key direction for overcoming the difficulties in pancreatic cancer treatments. This review summarizes recent research advances in the applications of nanomaterials for pancreatic cancer therapy, with a focus on diverse antitumor methods, discussing therapeutic mechanisms mediated by ferroptosis, cuproptosis, and pyroptosis during the treatment of pancreatic cancer, treatments induced by energy transfer involving photodynamic, sonodynamic, and photothermal therapies, gene regulatory approaches such as gene editing and delivery, immunotherapeutic mechanisms for remodeling the immune micro-environment, and strategies to enhance the efficacy of traditional chemotherapy. Additionally, this review highlights current research and development trends, as well as the application of nanomaterials with multi-mechanism synergy, offering a reference for the clinical translation of nanomaterials in pancreatic cancer treatment.

Four-dimensional flow magnetic resonance imaging (4D Flow MRI) is a non-invasive quantitative blood flow technique that integrates three-dimensional spatial information with the temporal dimensions. By employing three-directional velocity encoding, it enables visualization and multi-parametric quantification (e.g. peak velocity, energy loss, wall shear stress) of blood flow throughout the entire cardiac cycle.Through parameters such as direct flow ratio and energy loss, it can detect subclinical injury at an early stage, thereby providing crucial hemodynamic information complementary to existing structural imaging. This paper systematically reviews the technical features, cardiovascular applications, and future prospects of 4D Flow MRI, with a focus on its applications in congenital heart disease, pulmonary hypertension, and heart failure and cardiomyopathy. 4D Flow MRI is gradually emerging as an important tool in cardiovascular disease evaluation, and with continuous technological breakthroughs, its clinical value is expected to be further validated and extended in early disease diagnosis, treatment guidance, and prognostic monitoring.