BMC Medical Imaging最新文献

{"title":"Determinants of successful midsagittal visualization of the fetal corpus callosum using 2D transabdominal ultrasound.","authors":"Aylin Yılmaz, Didar Kurt","doi":"10.1186/s12880-026-02580-0","DOIUrl":"10.1186/s12880-026-02580-0","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate factors influencing visualization of the fetal corpus callosum (CC) during mid-trimester ultrasound examinations, with particular focus on gestational age, fetal presentation, and maternal body mass index (BMI).</p><p><strong>Methods: </strong>This observational study included 860 singleton pregnancies undergoing mid-trimester ultrasound examination between 20 + 0 and 28 + 0 weeks' gestation. Fetuses with central nervous system or major structural anomalies were excluded. Assessment of the corpus callosum was performed using two-dimensional (2D) transabdominal ultrasound. Visualization was defined as clear identification of the corpus callosum in the true midsagittal plane. Associations between CC visualization and gestational age, fetal presentation, and maternal BMI were analyzed using chi-square tests and multivariate logistic regression.</p><p><strong>Results: </strong>The corpus callosum was successfully visualized in 748 of 860 examinations (87.0%). Visualization rates decreased significantly with advancing gestational age (p < 0.001). Successful visualization was lowest in cephalic presentation (79.8%) and highest in breech presentation (97.7%) (p < 0.001). Maternal obesity (BMI ≥ 30 kg/m²) was associated with reduced visualization rates compared with BMI < 30 kg/m² (80.2% vs. 89.5%, p < 0.001). In multivariate analysis, breech presentation remained an independent predictor of successful visualization compared with cephalic presentation (adjusted OR 9.35, 95% CI 4.25-20.57, p < 0.001). Maternal obesity independently reduced the likelihood of visualization (adjusted OR 0.49, 95% CI 0.32-0.76, p = 0.001). Each one-week increase in gestational age was associated with a 15% decrease in visualization likelihood (adjusted OR 0.85, 95% CI 0.78-0.92, p < 0.001). Gentle hand manipulation was required in 423 examinations (49.2%) and was significantly more frequent in cephalic presentation than in breech or transverse presentation (62.7% vs. 30.2% and 39.0%, respectively; p < 0.001).</p><p><strong>Conclusion: </strong>Transabdominal ultrasound enables midsagittal visualization of the fetal corpus callosum in the majority of mid-trimester examinations. Fetal presentation, gestational age, and maternal obesity exert a significant influence on visualization rates; in particular, maternal obesity and increasing gestational age are associated with a reduced probability of successful assessment. Gentle hand manipulation was frequently required, highlighting the technical challenges of midsagittal corpus callosum imaging during routine examinations.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13644224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI analysis of androgen insensitivity syndrome.","authors":"Yijuan Xiang, Peng Wen, Rui Hu, Guanghui Yang","doi":"10.1186/s12880-026-02582-y","DOIUrl":"10.1186/s12880-026-02582-y","url":null,"abstract":"<p><strong>Background: </strong>Androgen insensitivity syndrome (AIS) is a rare disorder of sex development, and accurate preoperative imaging assessment is crucial for clinical decision-making. This study aimed to describe the magnetic resonance imaging (MRI) features of AIS, correlate imaging findings with surgical and pathological results, and evaluate the preoperative value of MRI in AIS.</p><p><strong>Methods: </strong>This retrospective study analyzed the clinical data and preoperative MRI images of 12 patients with androgen insensitivity syndrome diagnosed through chromosomal karyotyping and confirmed by surgical exploration at our hospital between December 2018 and April 2026; gonadal pathological results were available for most gonads.</p><p><strong>Results: </strong>All patients were definitively diagnosed with AIS based on chromosomal karyotyping, laboratory examinations, as well as surgical exploration, with pathological confirmation obtained for most gonads, including 11 cases of complete androgen insensitivity syndrome (CAIS) and 1 case of partial androgen insensitivity syndrome (PAIS). MRI revealed a blind-ending vagina and dysplastic gonads located in the pelvis or inguinal region in all patients. The gonads showed isointense signal on T1-weighted images (T1WI), slightly hyperintense signal on T2-weighted images (T2WI), hyperintensity on diffusion-weighted images (DWI), and hypointensity on apparent diffusion coefficient (ADC) maps. Mild to moderate enhancement was observed on contrast-enhanced images. MRI achieved accurate localization of all dysplastic gonads confirmed by surgical exploration in this cohort (21/21). MRI demonstrated the absence of the uterus and ovarian structures in the pelvic cavity of patients with CAIS. However, MRI in the patient with PAIS revealed a short penis and a strip-like abnormal signal behind the bladder, which was confirmed intraoperatively as tissue resembling the uterus. Only one patient showed severe right perigonadal hydrocele on MRI, with an underlying compressed nodule that exhibited isointense signal on T1WI, slightly hyperintensity on T2WI, hypointensity on DWI, hyperintensity on ADC maps, and rapid marked enhancement. Histopathological examination ultimately confirmed a Sertoli-Leydig cell tumor (SLCT).</p><p><strong>Conclusions: </strong>MRI is a reliable preoperative imaging modality for the localization of dysplastic gonads in AIS, with non-contrast sequences generally being sufficient. Contrast-enhanced imaging may offer complementary information for identifying potential Müllerian remnants or concurrent lesions in selected cases.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13640112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance evaluation of domain-specific and general-purpose AI models for chest radiograph interpretation: a comparative study.","authors":"Tae-Hoon Kim, Jun Hyung Hong, Jihun Hyun, Sang Gook Song, Eun Ju Yoon, Eunji Jeong, Jin Woong Kim, Hyung Joong Kim, Hyun Chul Kim","doi":"10.1186/s12880-026-02557-z","DOIUrl":"10.1186/s12880-026-02557-z","url":null,"abstract":"<p><strong>Background: </strong>Chest radiography remains the most widely used imaging modality worldwide; however, its interpretation is inherently challenging because of overlapping anatomical structures and subtle findings. Recent advances in multimodal large language models (LLMs) have enabled automated radiology report generation, yet their clinical performance relative to domain-specific medical AI systems remains insufficiently validated.</p><p><strong>Objectives: </strong>This study aimed to evaluate the performance and clinical applicability of a domain-specific multimodal AI model (M4CXR) compared with a general-purpose LLM (ChatGPT-4o) for chest radiograph interpretation.</p><p><strong>Methods: </strong>In this retrospective study, 500 anonymized chest radiographs from a single tertiary care center were analyzed. Four board-certified radiologists independently evaluated AI-generated reports from both models. Key outcomes included key finding detection (categorized as complete, partial, or inconsistent), report generation time, and report discrepancies assessed using the RADPEER scoring system. Agreement between original and M4CXR-assisted RADPEER scores was assessed using intraclass correlation coefficients and weighted Cohen's kappa. Statistical analyses included paired t-tests, and chi-square tests.</p><p><strong>Results: </strong>M4CXR demonstrated significantly higher report consistency than GPT-4o, with complete concordance observed in 55.8% versus 19.8% of cases, and lower inconsistency rates (25.2% vs. 46.4%, P<.001). The use of M4CXR significantly reduced report generation time compared with unaided interpretation (16.3 ± 12.9 s vs. 179.2 ± 50.4 s, P<.001). RADPEER-based discrepancy analysis revealed no significant differences between original and AI-assisted interpretations. Agreement between original and M4CXR-assisted RADPEER scores showed good reliability (ICC = 0.701), and weighted kappa analysis showed substantial agreement (κ_w = 0.652).</p><p><strong>Conclusions: </strong>Domain-specific multimodal AI model evaluated in this study demonstrated higher diagnostic consistency than the general-purpose LLM evaluated under the study conditions. These findings suggest the potential of specialized AI models as viable assistive tools, while highlighting the complementary utility of general-purpose LLMs in broader clinical contexts. Future integration should prioritize human-AI collaboration and prospective multi-center validation.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individualized metabolic brain network alterations in diffuse large B-cell lymphoma: an ¹⁸F-FDG PET study.","authors":"Yelu Gu, Fan Yang, Hailun Zhang, Jingke Xiao, Jiangyan Liu","doi":"10.1186/s12880-026-02562-2","DOIUrl":"10.1186/s12880-026-02562-2","url":null,"abstract":"<p><strong>Objective: </strong>To investigate cross-sectional differences in individualized metabolic brain network topology between patients with diffuse large B-cell lymphoma (DLBCL) and healthy controls, and to explore the associations between network topology and cognitive performance.</p><p><strong>Methods: </strong>A total of 104 patients with DLBCL and 33 healthy controls were prospectively enrolled. All participants underwent whole-body ¹⁸F-FDG PET/CT between January 2024 and January 2026. Patients were categorized into baseline and post-chemotherapy groups according to treatment status. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Functional Assessment of Cancer Therapy-Cognitive Function Scale (FACT-Cog). Individual metabolic brain networks were constructed using the Kullback-Leibler Divergence Similarity Estimation (KLSE) method. Graph theory was applied to analyze global and nodal topological properties. Group comparisons and correlation analyses were conducted to evaluate associations between network metrics and cognitive scores (P < 0.05).</p><p><strong>Results: </strong>We included 137 participants: 104 patients (mean age, 55.6 ± 11.7 years; 57 males) and 33 healthy controls (mean age, 50.3 ± 11.8 years; 17 males). Compared with controls, both baseline and post-chemotherapy patients showed significantly lower MMSE and MoCA scores (P < 0.001). There were no significant differences in cognitive scale scores between the two patient groups. Meanwhile, all groups exhibited small-world properties (γ > 1, λ ≈ 1, σ > 1), with no significant differences in global network metrics (P > 0.05). At the nodal level, baseline patients showed increased nodal degree and efficiency specifically in frontal regions, and decreased betweenness centrality in bilateral parietal regions. In cross-sectional comparisons, post-chemotherapy patients exhibited decreased degree centrality and nodal efficiency in prefrontal regions compared with baseline. In contrast, temporal, occipital, and selected subcortical regions showed increased nodal degree or efficiency. Furthermore, nodal degree, efficiency, and betweenness centrality in these regions were significantly correlated with cognitive scores.</p><p><strong>Conclusion: </strong>DLBCL patients maintained global network organization but demonstrated specific regional topological changes compared to healthy controls. The observed nodal differences between baseline and post-chemotherapy patients were correlated with cognitive performance. Individualized ¹⁸F-FDG PET/CT assessment of metabolic brain networks offers valuable insight into cognitive impairment in DLBCL.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13640372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking vertebral trabecular structure to growth rate: clinical and developmental insights.","authors":"Merve Gonca, Mehmet Fatih Sert, Dilara Nil Günaçar, Taha Emre Köse, Büşra Beşer Gül, Samet Uygun","doi":"10.1186/s12880-026-02583-x","DOIUrl":"10.1186/s12880-026-02583-x","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the effectiveness of chronological age (CA), sex, and fractal dimension (FD) values in classifying skeletal maturation stages using multinomial logistic regression analysis.</p><p><strong>Materials and methods: </strong>Lateral cephalometric radiographs and hand-wrist radiographs (HWRs) of 923 individuals (409 boys and 514 girls) aged 7-18 years were analyzed. Nine regions of interest located in the C2, C3, and C4 vertebral bodies were selected for the fractal analysis. Skeletal maturation stages were determined using Fishman's skeletal maturity indicator (SMI) method and categorized into accelerating (n = 201; 141 boys and 60 girls), high (n = 334; 165 boys and 169 girls), and decreasing (n = 388; 103 boys and 285 girls) growth velocity groups. Multinomial logistic regression was used to evaluate the associations of CA, sex, and FD values with maturation group membership. Relative risk ratios (RRRs) and 95% confidence intervals (CIs) were calculated to assess the effects of CA, sex, and FD values on maturation stage classification.</p><p><strong>Results: </strong>The full model was statistically significant compared with the intercept-only model and achieved an overall apparent classification accuracy of 77.8%. The age-and-sex-only model had a Nagelkerke R² of 0.768 and an accuracy of 77.2%, whereas the full model including FD variables had a Nagelkerke R² of 0.779 and an accuracy of 77.8%. These results indicate that FD measurements provided a modest additional contribution beyond CA and sex. CA and sex were the most stable predictors. Among FD variables, C2 anterior FD showed a significant overall effect, while C4 medial and C4 posterior FD showed more limited, category-specific associations.</p><p><strong>Conclusion: </strong>CA and sex were the primary predictors of maturation group membership. FD values from selected cervical vertebral regions may provide supplementary information, but their contribution was modest and should be interpreted cautiously until externally validated.</p><p><strong>Clinical relevance: </strong>Cervical vertebral fractal analysis may provide supplementary information for skeletal maturation assessment when combined with chronological age and sex; however, external validation is required.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning algorithm for coronary heart disease prediction based on retinal fundus photographs and optical coherence tomography.","authors":"Ran Yan, Xiaoxiao Guo, Jiang Zhu, Yan Zhu, Tingting Hong, Weijie Zhang, Zongqing Ma, Xinxiao Gao","doi":"10.1186/s12880-026-02551-5","DOIUrl":"10.1186/s12880-026-02551-5","url":null,"abstract":"<p><strong>Background: </strong>Coronary heart disease (CHD) is a major cause of mortality worldwide. This study aimed to develop and validate a multimodal deep learning algorithm using retinal imaging to assist in CHD risk assessment.</p><p><strong>Methods: </strong>In this retrospective study, we developed a deep learning algorithm that integrates retinal fundus photographs and optical coherence tomography (OCT) images. A clinical nomogram was also developed by combining the imaging-based predictions with clinical risk factors. Model performance was evaluated on internal validation and test cohort using receiver operating characteristic (ROC) analysis and calibration curves.</p><p><strong>Results: </strong>The algorithm was developed and validated using a dataset of 505 patients, which included 282 with CHD. On the training cohort, the model achieved an area under the curve (AUC) of 0.9954 (95% CI: 0.9904-1.0000). On the independent validation and test cohorts, the model achieved AUCs of 0.9834 (95% CI: 0.9556-1.0000) and 0.9138 (95% CI: 0.8404-0.9871), respectively. The nomogram demonstrated an AUC of 0.9963 (95% CI, 0.9923-1.0000) on the training cohort, and AUCs of 0.9423 (95% CI, 0.8626-1.0000) and 0.9153 (95% CI, 0.8289-1.0000) on the internal validation and test cohort, respectively.</p><p><strong>Conclusions: </strong>This study demonstrates the feasibility of using a deep learning algorithm based on retinal imaging for CHD risk stratification. Future prospective, multicenter studies are needed to validate these findings and evaluate their potential clinical utility.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13390318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-attention temporal graph neural network on resting-state fMRI dynamic functional connectivity identifies risk-related patterns of migraine chronification.","authors":"Chun-Yang Xu, Song-Hua Zhan, Zhen Gong, Min Zhang, Yu Wu, Rong-Fang Guo, Ying-Nan Kong, Yichen Huang, Wen-Li Tan, Yu-Chan Yang","doi":"10.1186/s12880-026-02567-x","DOIUrl":"10.1186/s12880-026-02567-x","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Migraine chronification remains difficult to characterize at the individual level, and robust baseline imaging markers associated with subsequent conversion from episodic migraine (EM) to chronic migraine (CM) are lacking. Resting-state functional MRI (rs-fMRI) dynamic functional connectivity (dFC) captures transient brain network interactions, but conventional approaches provide limited interpretability for clinical translation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We enrolled 195 participants at baseline, including 95 healthy controls (HC) and 100 patients with episodic migraine at baseline. All migraine patients underwent baseline rs-fMRI during the interictal phase and were followed for 12 months; 70 remained EM (non-converters) and 30 converted to CM. Sliding-window dFC matrices (55-TR window, 2-TR step) were constructed from 142 regions of interest and represented as temporal graph sequences. We developed an interpretable temporal graph neural network integrating a two-layer graph isomorphism network, a gated recurrent unit, and dual spatial-temporal attention mechanisms to quantify node-level (\"where\") and window-level (\"when\") importance. A multilayer perceptron performed three-class discrimination among HC, EM non-converters, and CM converters. Model performance was evaluated using stratified ten-fold cross-validation. Edge-wise ANCOVA controlling for age, sex, and mean framewise displacement with false discovery rate correction was applied to dFC matrices at key temporal-attention windows and within k-means-derived dFC states.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Four recurrent dFC states were identified. State 3 showed the highest fractional occupancy and mean dwell time and was the only state with significant between-group edge-wise differences after correction. Compared with HC, migraine groups exhibited altered connectivity involving sensory, attention, default-mode, and subcortical systems. CM converters showed additional baseline abnormalities involving sensorimotor-visual, default-mode, ventral attention, and limbic-related circuits. Spatial attention shifted from occipital-frontoparietal hubs in HC to frontal-insular prominence in EM non-converters and a frontoparietal-limbic configuration in CM converters. Temporal attention peaked at window 23, where chronification-related abnormalities were most evident. The model achieved AUCs of 0.817(HC), 0.832(EM non-converters), and 0.874(CM converters), with a macro-AUC of 0.841 (95% CI 0.77-0.91). Findings were robust across alternative window lengths and step sizes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This rs-fMRI dFC-based temporal graph learning framework identified baseline spatiotemporal network patterns associated with subsequent migraine chronification and generated individualized time-resolved importance maps. Although the model showed promising internal discriminative performance, these findings should be interpreted as risk-related pattern identification","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13647739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of compressed-sensing cardiac accelerated cine MR in evaluating left ventricular function: a systematic review and meta-analysis.","authors":"Zhaozhao Wang, Huihui Kong, Zekun Zhou, Anqi Yang, Zien Zhou, Chi Wang, Jing An, Yi He","doi":"10.1186/s12880-026-02522-w","DOIUrl":"10.1186/s12880-026-02522-w","url":null,"abstract":"<p><strong>Objective: </strong>To assess the consistency of left ventricular (LV) characteristics on cine cardiac magnetic resonance (CMR) images acquired from compressed-sensing (CS) accelerated cine versus standard segmented cine (Std-cine) sequences, both utilizing a balanced steady-state free precession (bSSFP) readout.</p><p><strong>Materials & methods: </strong>We conducted a systematic review and meta-analysis. Electronic databases were searched for studies where both CS-cine and Std-cine sequences were performed for individual participants and LV characteristic data from the two sequences were reported separately. Two investigators independently assessed the eligibility of the retrieved studies and extracted data. Key outcomes included LV ejection fraction (LVEF), volume, mass, and image quality. We performed subgroup analyses for children, breath control, arrhythmia, and MRI field intensity.</p><p><strong>Results: </strong>Thirty-five studies were included. The LVEF (%) obtained from CS-cine imaging did not differ from that obtained from Std-cine imaging (standardized mean difference [SMD]: -0.05; 95% confidence interval [95%CI]: -0.11, 0.01; p = 0.12; I² = 0.0%). No other LV parameters differed (I² = 0.0% to 0.0%), nor did heterogeneity differ across subgroups by MR field intensity, breath-hold requirements, children, or arrhythmia (for inter-subgroup heterogeneity, all p < 0.05). The overall image quality was comparable between the two sequences (SMD: -0.36; 95%CI: -0.65, 0.08; p < 0.05; I² = 37%). The difference across participants with arrhythmias (SMD: -0.04; 95%CI: -1.26, 1.18; p = 0.95; I² = 87%) and without arrhythmias (SMD: -0.63; 95%CI: -1.05, -0.21; p = 0.003; I² = 91%) or with no relevant information on arrhythmias (SMD: -0.17; 95%CI: -0.51, 0.18; p = 0.35; I² = 87%) may have accounted for the high heterogeneity.</p><p><strong>Conclusions: </strong>CS-accelerated cine imaging and standard segmented cine imaging did not differ in their ability to evaluate LV characteristics. The CS-cine images quality was acceptable and superior to that of conventional cine images for arrhythmias.</p><p><strong>Prospero registration number: </strong>CRD42022328219.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13689335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carotid body measurement by computed tomography angiography: a cross-sectional population-based study.","authors":"Negin Faghih Dolatabadi, Paria Bolourinejad, Fatemeh Dashti, Somayeh Hajiahmadi","doi":"10.1186/s12880-026-02529-3","DOIUrl":"10.1186/s12880-026-02529-3","url":null,"abstract":"<p><strong>Background: </strong>Carotid bodies (CBs) are chemoreceptors located at the bifurcation of the common carotid arteries, playing an essential role in maintaining blood gas homeostasis and regulating cardiovascular function. CBs are increasingly affected in various systemic pathologies, particularly those involving sympathetic overactivation, including hypertension and heart failure. Despite CB hypertrophy in these conditions has been well-studied, normative data on dimensions remain scarce. This study aimed to establish reference ranges for CB size in a large Iranian population using computed tomography angiography (CTA).</p><p><strong>Methods: </strong>A cross-sectional study of 650 patients who underwent neck CTA (April 2023-April 2025) was conducted. After excluding 36 patients with carotid body tumors and further removing cases with missing, unmeasurable, or outlier measurements, 521 patients were included for final analysis. CB dimensions were measured in both the transverse (R1) and anteroposterior (R2) planes. Reference ranges were calculated and stratified by age, sex, and comorbidity status. In addition, subgroup analysis was performed to examine the influence of chronic diseases including hypertension, diabetes mellitus, hyperlipidemia, cardiovascular disease, chronic obstructive pulmonary diseases, cerebrovascular accident/transient ischemic attack (CVA/TIA), and thyroid disease.</p><p><strong>Results: </strong>In this study, the mean R1 of the CB was 2.66 ± 0.5 mm, and the mean R2 was 3.25 ± 0.61 mm. Notable size variations were observed across age groups, with the 60-70 years group showing a marginally significant larger R1 of 2.96 ± 0.45 mm and R2 of 3.54 ± 0.51 mm (p-value = 0.070). No statistically significant differences were observed across sex categories. Also, participants with chronic diseases exhibited slightly larger CBs compared to those without (R1 = 2.71 ± 1.20 vs. 2.57 ± 0.52, p-value = 0.128 and R2 = 3.30 ± 1.34 vs. 3.17 ± 0.63, p-value = 0.204, respectively).</p><p><strong>Conclusions: </strong>This study provides a comprehensive reference ranges for CB dimensions in a general population, with specific subranges based on age, sex, and comorbidities. These findings can enhance diagnostic accuracy and help distinguish between normal CB enlargement and pathological conditions.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13386705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Value of multiparametric MRI as a potential marker to differentiate membranous nephropathy from IgA nephropathy.","authors":"Rongchao Shi, Hao Wang, Min Li, KaiXin Li, Huai Yang, JinXia Zhu, Bernd Kuehn, Marcel Dominik Nickel, Dawei Yang, Weikang Guo, Hui Xu, Zhenghan Yang","doi":"10.1186/s12880-026-02516-8","DOIUrl":"10.1186/s12880-026-02516-8","url":null,"abstract":"<p><strong>Background: </strong>Membranous nephropathy (MN) and IgA nephropathy (IgAN) exhibit similar clinical symptoms but differ substantially in treatment strategies and prognoses, highlighting the need for a non-invasive and precise diagnostic method. Multiparametric MRI (mpMRI) has shown great potential for the non-invasive assessment and quantification of renal function. This study aims to explore the potential value of mpMRI in distinguishing between MN and IgAN.</p><p><strong>Methods: </strong>Cortical renal blood flow (cRBF), cortical true diffusion coefficient (cD), cortical pseudo-diffusion coefficient (cD*), cortical perfusion fraction (cf), cortical R2* (cR2*), cortical T1 mapping (cT1), cortical mean diffusivity (cMD), and cortical mean kurtosis (cMK) were prospectively measured in 58 patients with MN, 47 patients with IgAN, and 75 healthy controls. One-way analysis of variance was used to compare MRI parameters among the three groups. The correlations between laboratory data and MRI parameters were evaluated using Spearman correlation analysis. Logistic regression analysis was performed to construct diagnostic models. The diagnostic performances of models for differentiating MN and IgAN were assessed using receiver operating characteristic curves with area under the curve (AUC).</p><p><strong>Results: </strong>Compared with the IgAN group, patients with MN were older and had higher cRBF, cf, and cT1 values (all P < 0.050), but cMK value was significantly lower than that in IgAN (P = 0.009). In the MN group, a significant correlation was observed between eGFR and cf. (r = 0.61, P < 0.001), whereas in the IgAN group, eGFR showed a significant correlation with cRBF (r = 0.51, P < 0.001). Age, eGFR, 24 h urinary protein, and cMK demonstrated the ability to distinguish between MN and IgAN in both univariable and multivariate logistic regression analysis and were used to construct diagnostic models. Among the multiparametric models, the combination model incorporating age, eGFR, 24 h urinary protein, and cMK exhibited the highest AUC of 0.92 (95% confidence interval [CI], 0.87-0.97), with a sensitivity of 0.91 (0.81-0.96) and a specificity of 0.81 (0.67-0.90).</p><p><strong>Conclusion: </strong>An integrated model combining mpMRI and clinical variables provides a non-invasive method for differentiating MN from IgAN with high diagnostic accuracy.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13632210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}