Objective To investigate the application value of KARL 3D iterative reconstruction technology in chest breath-hold PET/CT scanning. Methods Ninety-three patients with suspected pulmonary nodules undergoing chest breath-hold PET/CT scans at the First Affiliated Hospital of Shandong First Medical University from March 2022 to March 2023 were prospectively enrolled. Participants were stratified into three groups (10, 20, 30 mAs) based on tube current settings for breath-hold attenuation correction CT (ACCT). ACCT images acquired under different low-dose protocols were reconstructed using both KARL 3D and conventional filtered back projection (FBP) algorithms. Quantitative comparisons of CT_mean, noise (SD), and signal-to-noise ratio (SNR) were performed for the aorta, main pulmonary artery, and axillary adipose tissue between reconstruction methods. Comparative analysis was further conducted between KARL 3D-reconstructed images and FBP-reconstructed diagnostic-dose images (FBP-reconstructed 130-mAs images, FBP₁₃₀) regarding SD, SNR, and effective dose (ED). Results At all dose levels, no statistically significant differences were observed in CT mean between KARL 3D and FBP reconstructions (P>0.05). After KARL 3D reconstruction, the standard deviation (SD) decreased and the SNR increased in the images, with statistically significant differences (P<0.001). For the 10 mAs and 20 mAs groups, KARL 3D-reconstructed images showed statistically significant differences in SD, SNR and ED compared with FBP₁₃₀ (P<0.001). The 30 mAs protocol combined with KARL 3D achieved comparable SD and SNR to FBP₁₃₀ (P>0.05), while reducing ED by 68%. Conclusion The 30 mAs protocol with KARL 3D (grade 5) reconstruction produces diagnostic-quality images equivalent to conventional high-resolution CT-FBP reconstructions, simultaneously achieving attenuation correction. This approach reduces radiation exposure by approximately 76% compared with conventional diagnostic CT plus low-dose ACCT protocols.

Objective To evaluate the effectiveness of an artificial intelligence (AI) image-assisted diagnostic system in the prediction of pulmonary nodules and its clinical application value. Methods A total of 212 patients with definitive pathologic diagnoses of pulmonary nodules underwent analysis of their preoperative chest CT images, which were provided in DICOM format, using the AI-assisted diagnostic system. The diagnostic effectiveness of the AI model and Lung-RADS were compared in predicting of benign and malignant pulmonary nodules with different clinical and imaging characteristics. Results The AI model demonstrated higher diagnostic accuracy than Lung-RADS in distinguishing between benign and malignant pulmonary nodules (70.75% vs 60.85%, P<0.05). Results of the stratified analysis were as follows. By age: The AI model showed higher accuracy than Lung-RADS for patients aged 50-59 years (70.31% vs 53.13%, P<0.05). By nodule position: There were no significant differences between he AI model and Lung-RADS (P>0.05). By nodule density: The AI model showed higher accuracy than Lung-RADS for the mixed-ground glass nodules (74.51% vs 49.02%, P<0.05). By nodule size: The AI model showed higher accuracy than Lung-RADS for the nodules measuring 10-19 mm in diameter (74.75% vs 66.67%, P<0.05). By malignant pathology: The AI model exhibited higher accuracy in predicting adenocarcinoma nodules compared to Lung-RADS (77.52% vs 62.79%, P<0.05). Conclusion The AI image-assisted diagnostic system surpasses Lung-RADS in assessing the benign and malignant pulmonary nodules. With ongoing technological advancements, it has the potential to provide a reliable foundation for the early, non-invasive diagnosis of pulmonary nodules.

Objective To explore the value of renal dynamic imaging combined with urinary microalbumin (Um-Alb) and urinary β2-microglobulin (Uβ₂-MG) in the diagnosis of renal involvement in primary Sj?gren's syndrome (pSS). Methods A retrospective analysis of 52 pSS patients attending our hospital from January 2019 to December 2023 included 33 patients in impaired renal function group and 19 in unimpaired renal function group. The renal function curve parameters, ERPF, Um-Alb, Uβ₂-MG were collected and compared between the two groups, the diagnostic efficacy of ERPF, Um-Alb, Uβ₂-MG and the three combination on pSS renal impairment was compared, and the combined model of the best diagnostic efficacy was constructed and visualized with the Nomogram. Results Between the two groups, time to peak wasn't prolonged, while pSS duration, half time of excretion and 20 min residual rate increased, ERPF was lower, Um-Alb and Uβ₂-MG were higher than unimpaired renal function group, and the difference was statistically (P<0.05). ERPF, Um-Alb and Uβ₂-MG had higher diagnostic efficacy of pSS renal damage, with AUC of 0.813,0.859 and 0.850 respectively and combined AUC of 0.992. Conclusion Renal dynamic imaging, ERPF combined with Um-Alb and Uβ₂-MG can effectively diagnose pSS renal impairment, clinicians can predict the risk of pSS renal impairment and intervene early according to nomogram model.

Objective To investigate the predictive value of diverse expression parameters of ¹⁸F-FDG and ¹⁸F-PSMA-1007 PET/CT imaging for pathological grading of prostate cancer. Methods A retrospective analysis was carried out on the clinical characteristics, as well as ¹⁸F-FDG and ¹⁸F-PSMA-1007 PET/CT imaging data of 235 patients who were initially diagnosed with prostate cancer at the People's Hospital of Xinjiang Uygur Autonomous Region from July 2022 to May 2024. The enrolled patients were classified into the low-grade group and high-grade group according to the Gleason grading system. Mann-Whitney U test was employed to compare the differences in total prostate specific antigen (TPSA) and each expression parameter of PET/CT before treatment between the two groups. Spearman's rank correlation coefficient was utilized to analyze the correlation between different expression parameters and the International Society of Urological Pathology (ISUP) grouping, as well as pre-treatment TPSA values. A binary multifactorial logistic regression model was adopted to identify the independent predictors of prostate cancer pathological grading. Additionally, ROC curves were used to evaluate the predictive efficacy of different expression parameters for high-grade prostate cancer. Results Statistically significant differences was observed in the different expression parameters between the two groups (P<0.001). Spearman's rank correlation coefficient analysis indicated that each expression parameter of both imaging modalities was positively correlated with ISUP grading and pre-examination TPSA values. Binary multifactorial logistic regression analysis revealed that only SUV_max2 of ¹⁸F-PSMA-1007 PET/CT imaging was an independent predictor of ISUP grading, with an OR of 0.54(0.293-0.993, P=0.047). PSMA-TV and TL-PSMA had the largest areas under the ROC curve (0.950 and 0.928, respectively, P<0.001). Conclusion ¹⁸F-PSMA-1007 PET/CT expression parameters, namely SUV_max2, PSMA-TV, and TL-PSMA, exhibit greater advantages in the early prediction of prostate cancer pathologic grading.

Objective To investigate the predictive potential of building a nomogram model for testicular torsion (TT) by combining clinical characteristics and ultrasound signals. Methods The clinical data of 272 patients with scrotal pain in Inner Mongolia People's Hospital from March 2011 to April 2024 were retrospectively analyzed. According to the 7:3 random machine, they were divided into training set (n=190) and validation set (n=82). The training set consisted of 97 non-TT patients and 93 TT patients, based on the findings of surgical exploration and follow-up. The validation set consisted of 41 non-TT patients and 41 TT patients. The clinical information and several ultrasonic characteristics of the patients were compared using the training set. R software was used to create the risk prediction model of TT visual nomogram in scrotal discomfort after univariate analysis and multivariate logistic regression were used to check for independent influencing factors. The model's performance was examined and assessed using the ROC curve, calibration, and decision curves. Results Age, internal testicular parenchymal echogenicity, testicular blood flow signal grade, Spermatic cord whirlpool, and neutrophil/lymphocyte ratio were all independent risk variables for TT prediction, according to the results of multifactorial logistic regression analysis (P<0.05). A nomogram prediction model was established using the aforementioned criteria. Both the validation set and the model training set had corresponding area under the curves of 0.980 and 0.995. The calibration curve demonstrated that the model's TT prediction probability in both the training and validation sets tended to match the likelihood of actual occurrence. In the training and validation sets, the average absolute error between the expected and actual outcomes was 0.018 and 0.040, respectively. The decision curve demonstrates that the net benefit of predicting TT in the training set and the verification set is higher when the model's probability thresholds are 1%-100% and 1%-93%, respectively. Conclusion In addition to providing a visual aid for clinical decision-making, the nomogram model based on ultrasound indicators in conjunction with clinical factors can accurately estimate TT risk.

Objective To evaluate the efficacy of a machine learning model constructed based on MRI radiomics features in predicting the molecular subtyping of invasive breast cancer before surgery. Methods A retrospective analysis was conducted on 155 cases of invasive breast cancer patients who underwent radical mastectomy at our hospital from January 2023 to June 2024. Patients were divided into three groups according to postoperative immunohistochemical molecular subtyping results: Luminal type (including Luminal A and Luminal B), HER2-overexpressing type, and triple-negative type. Clinical pathological and MRI sign data of the three groups were compared; the lesions in the second phase of MRI images of all patients were manually segmented to extract ROI radiomics feature variables, and LASSO regression was used for feature variable dimensionality reduction and screening. All sample data were divided into training and testing sets in a 7:3 ratio; random forest, logistic regression, and support vector machine, three machine learning models, were used to model the screened radiomics feature variables, and ROC curves were plotted to evaluate the efficacy of different machine learning models in preoperative assessment of invasive breast cancer molecular subtyping. Results There was no statistical significance in general clinical pathological data and imaging indicators among the three groups (P>0.05); the confusion matrix showed that the diagnostic sensitivity, specificity, and accuracy of the random forest model were 96.55%, 93.33%, and 93.62%, respectively, significantly higher than those of the logistic regression and support vector machine models (P<0.05); ROC curve analysis showed that the random forest model had the highest predictive AUC of 0.903 (95% CI: 0.815-0.958), significantly higher than the logistic regression model's 0.680 (95% CI: 0.565-0.782) and the support vector machine model's 0.693 (95% CI: 0.579-0.793), with statistically significant differences (P<0.05). Conclusion The random forest model constructed based on MRI radiomics features and machine learning algorithms has high efficacy in predicting the molecular subtyping of invasive breast cancer before surgery.

Objective To optimize the STAR-VIBE sequence and explore its clinical application value for multi-phase dynamic contrast-enhanced liver imaging under free-breathing conditions. Methods Ninety-six patients with liver enhancement magnetic resonance scanning in Wuhan Central Hospital from March 2022 to July 2024 were included, and were divided into the group with normal breath-holding function (n=48) and the group with breath-holding obstacle (n=48) using group-control design according to whether the patients were able to hold their breath for 15 s. Both groups underwent free-breathing dynamic contrast-enhanced STAR-VIBE scanning with delayed-phase imaging, alongside conventional breath-hold T1-VIBE delayed enhancement scanning. The accuracy of dynamic phase visualization was evaluated using a double-blind method. Image quality of the delayed phase was quantitatively assessed using a 5-point scale, with statistical intergroup comparisons. Signal intensity (SI) values of liver parenchyma, erector spinae muscle, and background noise were measured at identical anatomical levels (avoiding vessels and lesions) on delayed-phase STAR-VIBE and T1-VIBE images. Liver signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for both sequences. Results Two cases exhibited indistinct arterial and portal venous phase differentiation, yielding an overall phase accuracy of 98%. In the normal breath-holding group, there was no statistically significant difference in image quality scores between STAR-VIBE and T1-VIBE in the delayed phase(P>0.05), both achieving good diagnostic quality (STAR-VIBE: 4.66±0.59, T1-VIBE: 4.83±0.37). In the impaired breath-holding group, scores differed significantly between the two sequences (P<0.05). Delayed-phase STAR-VIBE image quality remained consistently good across both groups, with no intergroup statistical difference (P>0.05). SNR and CNR analysis revealed no significant differences between STAR-VIBE and T1-VIBE in the normal breath-holding group (P>0.05), whereas significant differences were observed in the impaired breath-holding group (P<0.05). There was no statistical significance in SNR/CNR between the two groups (P>0.05). Conclusion The optimized STAR-VIBE sequence enables diagnostically acceptable multi-phase dynamic contrast-enhanced liver imaging under free-breathing conditions, providing an effective imaging evaluation protocol for patients with limited breath-holding capacity.

Objective To investigate the clinical factors influencing pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer (LARC). Methods A retrospective analysis was conducted on the clinical data of 122 LARC patients admitted to the First Affiliated Hospital of Bengbu Medical University from January 2017 to June 2023. Univariate analysis and binary logistic multivariate regression analysis were used to examine the clinical factors affecting pCR, while ROC curve analysis was further employed to validate the predictive accuracy of certain indicators for pCR. Results Among the 122 enrolled patients, 27 (22.13%) achieved pCR. Univariate analysis revealed that extramural venous invasion(EMVI), pretreatment carcinoembryonic antigen (CEA) level, perineural invasion, lymphovascular invasion, circumferential tumor extent, and tumor length were statistically correlated with pCR. The Hosmer-Lemeshow test showed a goodness-of-fit significance of 0.168. ROC curve analysis of MRI-measured tumor length yielded an AUC of 0.706, with a sensitivity of 85.3% and specificity of 59.3%, and the optimal cutoff value was 1.65 cm. For CEA in predicting pCR, the AUC was 0.632, with a sensitivity of 42.1% and specificity of 88.9%, and the optimal cutoff value was 4.955 ng/mL. Multivariate analysis identified lymphovascular invasion, circumferential tumor extent, and tumor longitudinal length as independent predictors of pCR. Conclusion Lymphovascular invasion, circumferential tumor extent, and tumor length may be critical determinants for achieving pCR after neoadjuvant chemoradiotherapy in LARC patients.

Objective To explore the application value of global longitudinal strain (GLS) and peak strain dispersion (PSD) combined with left ventricular pressure strain loop (PSL) technology in the early diagnosis of myocardial injury in systemic lupus erythematosus (SLE) patients. Methods We selected 74 SLE patients hospitalized at Yunnan 1^st People Hospital from August 2022 to July 2024, all undergoing cardiac ultrasound examinations. Exclude 5 patients who were unable to complete the analysis due to poor image quality. Additionally, 69 patients were graded and grouped based on the SLEDAI-2K score. Divided into group A with 22 cases (basically inactive and mildly inactive, score<9), group B with 21 cases (moderate activity, score 10-14), and group C with 26 cases (severe activity, score≥15), 36 outpatient health examiners were selected as the normal control group D, for statistical analysis of clinical baseline data and related echocardiographic measurement parameters. Results It showed a declining trend in GLS and an increasing trend in PSD among the four groups, with statistically significant differences (P<0.05) observed in groups B and C. GWE decreased among the four groups, while GWW, GNW and GSWW increased, with statistically significant differences (P<0.05) observed in groups B and C; Correlation analysis showed that GLS was negatively correlated with LVEF (r=-0.274, P<0.05); GWE, GWI, GCW, GPW, GSCW were negatively correlated with GLS (r=-0.584, -0.451, -0.431, -0.404, -0.428, P<0.05), while GWW,GNW, GSWW was positively correlated with GLS (r=0.390, 0.221, 0.362, P<0.05). GWE was negatively correlated with PSD (r=-0.621, P<0.05), while GWW, GNW, GSWW were positively correlated with PSD (r=0.567, 0.264, 0.442, P<0.05). Comparison of myocardial contraction function parameters between the SLE group (group A+B+C) and D group, showed that there was a statistically significant difference (P<0.05) between the two groups in terms of LVEF, GLS, PSD, GWE, GWI, GWW, GNW and GSWW. The AUC of GLS, PSD, GWE, GWW, GNW, and GSWW were 0.841, 0.714, 0.841, 0.763, 0.725 and 0.740, respectively. The AUC predicted by GLS, PSD and left ventricular PSL parameters GWE, GWW, GNW and GSWW was the highest (0.913), with a sensitivity of 85% and specificity of 96%. Conclusion Echocardiographic measurements using GLS and PSD combined with PSL parameters could be used to assess early myocardial injury in SLE patients, particularly in those with moderate to severe activity, which providing new sensitive indicators for clinical diagnosis of lupus myocardial lesions.

Objective To explore the metabolic parameters of ¹⁸F-FDG PET/CT in lung cancer, and analyze their differences and correlations with various histologic types, different histologic grades, and Ki-67 indices. Methods A retrospective analysis was conducted on the clinical data of 178 lung cancer patients (119 males and 59 females, 35-79 years old) who underwent ¹⁸F-FDG PET/CT examinations prior to surgical resection or pathological biopsy at our institution from January 2022 to October 2024. The SUV_max, SUV_mean, MTV and TLG were extracted from the imaging data. One-way analysis of variance was used to assess the differences in metabolic parameters across different pathological types and differentiation grades, while Spearman rank correlation analysis was employed to evaluate the correlation between metabolic parameters and the Ki-67 index. Results Significant differences in SUV_max, SUV_mean, MTV, TLG, and Ki-67 index were observed among squamous cell carcinoma, adenocarcinoma, and small cell lung cancer (P<0.001). Small cell lung cancer exhibited the highest values for these parameters, followed by squamous cell carcinoma. In overall analysis, SUV_max, SUV_mean, MTV, TLG, and Ki-67 in lung cancer all exhibited moderate to strong positive correlations (P<0.05). Among them, SUV_max had the strongest positive correlation with Ki-67 (r=0.522, P<0.05), followed by TLG (r=0.520, P<0.05). According to lung cancer subtypes,adenocarcinoma's SUV_max, SUV_mean, MTV and TLG demonstrated varying degrees of significant positive correlation with the Ki-67 index (P<0.001), with correlation coefficients (r) of 0.460, 0.414, 0.396, 0.408, respectively, where SUV_max showed the strongest correlation, followed by SUV_mean. In contrast, the metabolic parameters in squamous cell carcinoma and small cell carcinoma do not show a significant correlation with Ki-67 (P>0.05). The Ki-67 index in squamous cell carcinoma revealed significant differences among different differentiation grades (P<0.05), while no significant differences were noted for SUV_max, SUV_mean, MTV and TLG (P>0.05). In adenocarcinoma, significant differences were found in SUV_max, SUV_mean and Ki-67 index across various differentiation grades (P<0.05), whereas MTV and TLG showed no significant differences (P>0.05). Conclusion There were significant differences in ¹⁸F-FDG PET/CT metabolic parameters and Ki-67 among different pathological types of lung cancer, and there was a certain correlation between metabolic parameters and Ki-67. Significant differences in Ki-67 were observed between squamous cell carcinoma and adenocarcinoma. Additionally, the SUV_max and SUV_mean of adenocarcinoma showed significant variations across different differentiation levels.

Objective To analyze the effects of contrast media with different iodine concentrations on image quality and radiation dose of coronary computed tomography angiography (CCTA). Methods The data of 144 patients with coronary heart disease who were admitted to the Sichuan Science City Hospital from January 2020 to January 2024 were retrospectively analyzed. All patients underwent dual-source CCTA after admission, and the fixed iodine flow rate was controlled within the same weight range. The patients were divided into group A (n=48), group B (n=48) and group C (n=48) according to different iodine concentrations in contrast media. The iodine concentrations of contrast media used in group A, B and C were 270 mgI/mL, 320 mgI/mL, 350 mgI/mL, respectively. The subjective image quality, CT values of the aortic root, right coronary artery and proximal left anterior descending artery, image noise, signal-to-noise ratios (SNRs) and radiation doses under contrast media with different iodine concentrations were compared. Results There were no statistically significant differences among the three groups in terms of the distribution of excellent, good and moderate quality of subjective coronary artery display, CT values of the aortic root, right coronary artery and left anterior descending artery, SD value, SNR, CTDI, DLP, ED (P>0.05). Conclusion Under a fixed iodine flow rate, there is no significant difference in image quality and radiation dose of CCTA using contrast media with different iodine concentrations (270, 320, 350 mgI/mL). For patients without obesity or severe renal dysfunction prioritize, contrast media with low iodine concentration is recommended to reduce the risk of nephrotoxicity.

Objective To measure parameters related to right heart function using echocardiography, analyze the right heart function of patients with pulmonary hypertension (PH), and explore independent predictive factors for major cardiovascular adverse events (MACE) in PH patients. Methods A retrospective analysis was conducted on 71 patients with pulmonary hypertension who underwent examination in our hospital. All study subjects underwent two-dimensional and three-dimensional echocardiography, and right heart function parameters were recorded. The endpoint of follow-up is the occurrence of MACE. Independent sample t-test was used to compare right heart function parameters between the MACE group and the non MACE group. Cox proportional hazards regression model was used to analyze the independent predictive factors of MACE, and the optimal cutoff value was obtained based on the ROC curve to plot the Kaplan Meier survival curve. Results Compared with the non MACE group, patients in the MACE group showed an increase in PASP, RVEDVI, and RVESVI, while RVEF, ePAC, TAPSE, TAPSE/PASP, and RVFAC all decreased with statistical significance (P<0.05). Cox analysis showed that TAPSE/PASP and ePAC were independent predictive factors for MACE in PH patients. The Kaplan-Meier survival analysis results showed that the incidence of MACE increased when TAPSE/PASP<0.199 mm/mmHg and ePAC<0.138 cm/mmHg (P<0.05). Conclusion The parameters of right heart function in PH patients with MACE are already poor, while TAPSE/PASP and ePAC can independently predict the occurrence of MACE in PH patients. These indicators can help doctors better assess the patient's condition and prognosis.

Objective To utilize ultrasound radiomics technology combined with clinical indicators and contrast-enhanced ultrasound (CEUS) to construct a combined model, thereby improving the diagnostic accuracy of PCa in the gray zone. Methods The diagnostic accuracy of prostate cancer (PCa) within the prostate-specific antigen gray zone (4-10 ng/mL) is relatively low, and traditional methods struggle to effectively differentiate between benign and malignant lesions. This study aims to utilize ultrasound radiomics technology combined with clinical indicators and contrast-enhanced ultrasound (CEUS) to construct a combined model, thereby improving the diagnostic accuracy of PCa in the gray zone. Results Among the 137 patients, 55 were diagnosed with prostate cancer and 82 had benign lesions. Multivariate logistic regression analysis showed that free prostate-specific antigen and peak intensity were independent predictors of PCa in the gray zone (P<0.05). The combined model achieved AUC values of 0.965 and 0.893 in the training and validation sets, respectively, significantly outperforming the single models. The DCA curve indicated that the combined model had a higher net benefit at low-risk thresholds, and the calibration curve further confirmed its accuracy. Conclusion The combined model constructed using ultrasound radiomics, clinical indicators, and CEUS significantly improves the diagnostic accuracy of PCa in the gray zone, providing an effective auxiliary diagnostic tool for clinical practice.

Objective To identify sensitive radiomics features based on multiparametric magnetic resonance (mpMR) imaging for predicting progression-free survival (PFS) in patients with metastatic gastrointestinal stromal tumors (mGIST) following imatinib treatment. Methods A total of 122 pathologically confirmed mGIST patients from two centers were retrospectively collected and divided into a training set (n=83) and a test set (n=39). Three-dimensional regions of interest were outlined on mpMR images, and radiomics features were extracted. Features with poor repeatability or strong collinearity were excluded based on interclass correlation coefficients (ICC) and Pearson correlation coefficients. The least absolute shrinkage and selection operator (LASSO) regression method was applied for feature selection, and cutoff values for the selected features were determined. Kaplan-Meier curves were used to evaluate PFS, and ROC curves assessed the predictive value of the features for 3-year PFS in patients. Results The First-Order 10^th percentile@ADC, GLRLM GLN@DWI, and GLRLM RLNN@DWI features showed strong correlations with mGIST PFS. Significant differences in these three features were observed between patients with progressive disease and non-progressive disease (P<0.05). The First-Order 10^th percentile@ADC exhibited a negative correlation with PFS, with hazard ratios (HR) of 0.339(95% CI: 0.162-0.707) in the training set and 0.354 (95% CI: 0.146-0.858) in the test set. Both GLRLM GLN@DWI and GLRLM RLNN@DWI were positively correlated with PFS, with HRs of 2.300 (95% CI: 1.120-4.721) and 4.562 (95% CI: 2.370-8.783) in the training set, respectively, and HRs of 3.704 (95% CI: 1.471-9.328) and 7.024 (95% CI: 2.407-20.498) in the test set. Combining these three features provided strong predictive performance for 3-year PFS, with AUC of 0.909 (95% CI: 0.861-0.567) in the training set and 0.788 (95% CI: 0.698-0.877) in the test set. Conclusion Radiomics features of mGIST are associated with patient PFS and can serve as reliable predictors for the prognosis of imatinib-targeted therapy.

Objective To explore the value of high resolution MR Vascular wall imaging in the mechanisms and extent of cerebral vascular occlusions. Methods From May 2016 to January 2025, 50 patients diagnosed with cerebral infarction in our hospital, including 56 occluded cerebral vessels identified by time-of-flight magnetic resonance angiography(TOF MRA) underwent high-resolution vessel wall imaging (HRVWI) to analyze the pathological mechanisms and measure the occlusion length.The occlusion lengths were scored 1, 2, or 3 points based on their extent from shortest to longest. All patients underwent digital subtraction angiography (DSA) within one week after MRI examination, with recanalization therapy performed on 23 occluded vessels. Differences in occlusion length assessment between HRVWI and TOF MRA were compared. The Kappa consistency test was used to evaluate the agreement between these two imaging modalities and DSA. Results HRVWI clearly revealed the causes and lengths of vascular occlusions. The occlusion mechanisms included thrombosis (20 cases), atherosclerotic plaque (17 cases), thrombosis superimposed on atherosclerosis (9 cases), moyamoya disease (4 cases), vasculitis (1 case), dissection (3 cases), fusiform aneurysm with mural thrombosis (1 case), and restenosis after stenting for dissection (1 case). The occlusion length scores assessed by HRVWI and TOF MRA were 1.34±0.55 and 1.64±0.67, respectively, with a statistically significant difference (P<0.0001). The Kappa values for agreement between HRVWI/TOF MRA and DSA in assessing occlusion length were 0.964 and 0.559, respectively. The Kappa value for agreement between HRVWI and DSA in determining occlusion mechanisms was 0.975. Conclusion High resolution MR can accurately evaluate the mechanisms and extent of cerebrovascular occlusions.

Abdominal CT examinations, characterized by broad anatomical coverage and multiphase scanning protocols, pose significant challenges in radiation dose control. Merely adjusting hardware parameters often fails to balance radiation reduction with diagnostic image quality. As a core technology that breaks through traditional hardware limitations, reconstruction algorithms have shown great potential in the field of low-dose abdominal CT. This review systematically examines the developmental trajectory, clinical applications, and limitations of conventional filtered back projection (FBP), iterative reconstruction (IR) and deep learning image reconstruction algorithms in low-dose abdominal CT. A focused analysis is provided on the technical principles, clinical application value, and potential shortcomings of mainstream algorithms such as ASIR-V and TrueFidelity^TM, aims to establish a theoretical foundation for selecting optimal reconstruction protocols in clinical practice and proposes future directions for algorithm refinement (such as integration with spectral CT and photon-counting detector technologies).

Concomitant depression is extremely common among the patients with Parkinson's disease and greatly affects the quality of life and prognosis of Parkinson, at the same time,the importance of recognising and diagnosing depression in Parkinson's remains grossly underestimated in clinical practice. Abnormal structural and functional alterations of the nucleus accumbens, as a key component of the limbic system and reward circuits in the brain, are often regarded as a potential physiological mechanism leading to depression and other psychiatric disorders. In this paper, we would not only take the nucleus accumbens as an entry point, but also review from both structural and functional MRI technology aspects,and discusse the significance of abnormalities in the nucleus accumbens of patients of depression with Parkinson's disease,with the aim of helping clinicians better diagnose and pave new perspectives and ideas for future research work.

The tumor microenvironment plays an important role in tumorigenesis, development, metastasis, and drug sensitivity. Carbonic anhydrase IX (CAIX) is a metalloenzyme that is highly expressed in certain tumor microenvironments and is closely related to tumor cell genesis, development and drug resistance. Its high expression in tumors and low expression in normal tissues makes it a potential target for the diagnosis and treatment of a variety of tumors. With the in-depth research on antitumor drugs and radionuclide-targeted therapy targeting CAIX, the detection of CAIX expression using noninvasive radiopharmaceuticals has become more urgent in order to meet the strategy of diagnostic and therapeutic integration. In this paper, we summarize and review the research on CAIX-targeting radiopharmaceuticals in recent years, analyze and summarize the advantages and disadvantages of monoclonal antibodies, small molecules and peptides, as well as the prospects of their clinical application, so as to optimize the structure of the drugs, reduce the side effects and meet the clinical needs, and to accelerate the clinical application of these drugs.

As the number of patients with Alzheimer's disease (AD) continues to rise globally, there is an urgent clinical need for early diagnosis and treatment. Currently, traditional methods for confirming AD diagnosis, such as biomarker detection, still face issues such as invasiveness, radiation risks, and high costs. Given the pathological characteristic of iron deposition in the brains of AD patients, quantitative susceptibility mapping (QSM) can be employed for non - invasive and sensitive detection and quantification. This article reviews the application value of QSM in AD diagnosis, including its ability to reflect AD pathology, the distribution of iron deposition in brain regions, and its correlation with biomarkers. Additionally, it explores the advantages, limitations, and potential for assessing treatment efficacy of QSM.

Lower limb swelling and lymphedema are common clinical symptoms that significantly affect patients' quality of life. Early, timely, and accurate diagnosis is crucial, as it can markedly improve prognosis. With the continuous advancement of medical imaging technology, techniques such as ultrasound, CT, MRI and nuclear medicine imaging play an increasingly important role in diagnosing lower limb swelling and lymphedema. This article reviews the principles and characteristics of various imaging modalities and their diagnostic applications, as well as the prospects of radiomics and artificial intelligence in diagnosis.