BMC Medical Imaging最新文献

{"title":"Clinical application of radiofrequency echographic multi-spectrometry (REMS) for diagnosis and follow-up in several rare bone disorders: a case series.","authors":"Carla Caffarelli, Caterina Mondillo, Alessandro Versienti, Sara Gonnelli, Guido Cavati, Maria Dea Tomai Pitinca, Stefano Gonnelli, Antonella Al Refaie","doi":"10.1186/s12880-025-01924-6","DOIUrl":"10.1186/s12880-025-01924-6","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"382"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173405","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":"Comparison of radial versus cartesian k-space sampling in T1-weighted MRI: image quality assessment for contrast-enhanced thoracic spine transverse imaging.","authors":"Liuhong Zhu, Yanwei Wang, Weigen Yao, Funan Wang, Hao Liu, Xiaobo Qu, Jianjun Zhou","doi":"10.1186/s12880-025-01931-7","DOIUrl":"10.1186/s12880-025-01931-7","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"383"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173542","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":"Volumetric alterations of hippocampal and amygdala subfields in Azheimer's disease.","authors":"Qi Feng, Zhehao Zhang, Fengyi Dai, Yan Chen, Zhongxiang Ding, Luoyu Wang","doi":"10.1186/s12880-025-01923-7","DOIUrl":"10.1186/s12880-025-01923-7","url":null,"abstract":"<p><strong>Background: </strong>Numerous studies have shown that patients with Azheimer's disease (AD) or amnestic mild cognitive impairment (aMCI) have atrophy in the hippocampus and amygdala. However, there are few MRI studies that have jointly analyzed the volumes of the 19 hippocampal subfields and the 9 amygdala subfields.</p><p><strong>Materials and methods: </strong>In this cross-sectional study, we used the method of analysis of covariance (ANCOVA) to analyze the volume differences of hippocampal and amygdala subfields among three groups of 40 normal control (NC), 40 aMCI and 53 AD, and conducted correlation analysis between these subfield volumes with significant differences and the overall cognitive scores.</p><p><strong>Results: </strong>Our volumetric analyses revealed significant atrophy in 16 hippocampal subfields among three groups. However, no significant volume loss was observed in the amygdalar subfields after rigorous multiple-testing correction. Interestingly, the atrophy was mainly concentrated in the right hemisphere. In addition, our study also found that the volume of some subfields of the hippocampus was significantly positively correlated with MMSE and MoCA scores.</p><p><strong>Conclusion: </strong>These results suggest that volume reduction in some hippocampus subfields may serve as a biomarker reflecting cognitive decline in patients with AD and aMCI.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"378"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173488","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":"Pilot study of association between neonatal brain perivascular space volume and neurodevelopmental outcomes at 24 months.","authors":"Arum Choi, Dayeon Bak, Ah-Ra Cho, Hosna Asma-Ull, Yoonho Nam, Hyun Gi Kim","doi":"10.1186/s12880-025-01912-w","DOIUrl":"10.1186/s12880-025-01912-w","url":null,"abstract":"<p><strong>Introduction: </strong>Perivascular space (PVS) has recently gained attention as a neurological indicator. However, there is still limited research on the relationship between basal ganglia PVS (BG-PVS) volume and neurodevelopmental outcome of neonates. Therefore, this pilot study aimed to investigate the association between BG-PVS volume of neonates at term-equivalent age and their neurodevelopmental outcome at 24 months.</p><p><strong>Methods: </strong>This single-center retrospective pilot study included neonates who underwent brain MRI between 2019 and 2022 at term-equivalent age and had neurodevelopmental assessment at 24 months using Bayley Scales of infant development third (Bayley-III). Sample size was determined by feasibility constraints with recruitment of available neonates from our clinical population. BG-PVS volume was extracted from brain MRI using 3D T2-weighted images through a combination of computational processing and manual refinement. Multiple linear regression models were used to examine the associations between BG-PVS volume and three Bayley-III scores (cognitive, language, and motor), adjusting for postmenstrual age. BG-PVS volumes between neonates with normal and delayed development were compared using Wilcoxon rank-sum tests with False Discovery Rate correction for multiple comparisons.</p><p><strong>Results: </strong>A total of 14 neonates were included (8 [57%] males; median gestational age, 246 [243-254] days). There were negative associations between BG-PVS volume and cognitive (coefficient = -0.70, P = 0.04), language (coefficient = -0.69, P = 0.01), and motor (coefficient = -0.71, P = 0.02) development scores. Neonates with delayed development showed larger BG-PVS volumes compared to those with normal development for language (38 [27-45] mm³ vs. 18 [17-25] mm³, P = 0.02) and motor domains (50 [49-51] mm³ vs. 26 [20-36] mm³, P = 0.02). BG-PVS volume was larger in neonates with delayed cognitive development compared to those with normal development, but the difference was not significant (49 [40-50] mm³ vs. 25 [20-36] mm³, P = 0.06).</p><p><strong>Conclusion: </strong>In this pilot study, larger BG-PVS volume in neonates at term-equivalent age was associated with poorer developmental outcomes at 24 months. These preliminary findings suggest that BG-PVS volume may warrant further investigation as a potential early imaging biomarker for neurodevelopmental risk assessment in neonates, though larger studies are needed to confirm these associations.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"387"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173516","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":"Habitat subregions analysis based on neurite orientation dispersion and density imaging enhances isocitrate dehydrogenase genotyping in glioma.","authors":"Zheting Yang, Ruolan Lin, Zhenxing Wu, Yang Song, Guang Yang, Rifeng Jiang, Yunjing Xue, Yì Xiáng J Wáng","doi":"10.1186/s12880-025-01934-4","DOIUrl":"10.1186/s12880-025-01934-4","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"384"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173524","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":"New heights in CT differentiation of adrenal lesions and a rational definition of non-enhancement.","authors":"Lichun Liu, Fangmei Zhu, Zongfeng Niu, Zongyu Xie, Dengfa Yang, Jian Wang, Cheng Yan","doi":"10.1186/s12880-025-01916-6","DOIUrl":"10.1186/s12880-025-01916-6","url":null,"abstract":"<p><strong>Background: </strong>To explore the stratification and identification of adrenal lipid-poor adenomas (LPAs), adrenal cysts (ACs), and adrenal ganglioneuromas (AGNs) from each other using contrast-enhanced computed tomography (CT).</p><p><strong>Methods: </strong>Pathologically confirmed, 348 patients were categorized into Model 1 (260 LPAs, 34 ACs), Model 2 (260 LPAs, 54 AGNs), and Model 3 (34 ACs, 54 AGNs). Statistical analyses were performed on the differences in the degree of enhancement in the arterial/venous phase (DEap/DEvp) (in HU) and the corresponding graded variables for the arterial/venous phase (GVap/GVvp). Models were evaluated via receiver operating characteristic (ROC) curves, calibration curves, and the Hosmer‒Lemeshow (HL) test.</p><p><strong>Results: </strong>The values of the area under the curve (AUC) for DEap, DEvp, GVap, and GVvp in Models 1-3 were 0.996, 1.000, 0.993, and 0.999; 0.980, 0.978, 0.961, and 0.975; and 0.734, 0.892, 0.725, and 0.883, respectively. The p values of the HL test were 0.984, 1.000, and 0.113, respectively. The DEvp interval values (in HU) for the LPAs, ACs, and AGNs were [4.9, 190.2] HU, [-3.7, 4.2] HU, and [-4.8, 41.8] HU, respectively. The GVap and GVvp ranges for the LPAs, ACs, and AGNs were [1, 6], [0, 2], and [0, 2] and [1, 6], [0, 1], and [0, 5], respectively.</p><p><strong>Conclusions: </strong>DEvp enhanced discrimination in Models 1 and 3, whereas DEap performed better in Model 2. Lesions with DEvp < 4.5 HU are likely represent non-enhancing pathology (e.g., cysts). When both GVap and GVvp are 0, when both GVap and GVvp are [2, 6], and when GVap is [3, 6] and GVvp is 6, LPA, AC, and AGN are excluded.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"374"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173557","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":"Automated deep learning method for whole-breast segmentation in contrast-free quantitative MRI.","authors":"Weibo Gao, Yanyan Zhang, Bo Gao, Yuwei Xia, Wenbin Liang, Quanxin Yang, Feng Shi, Tuo He, Guangxu Han, Xiaohui Li, Xuan Su, Yuelang Zhang","doi":"10.1186/s12880-025-01928-2","DOIUrl":"10.1186/s12880-025-01928-2","url":null,"abstract":"<p><strong>Background: </strong>To develop a deep learning segmentation method utilizing the nnU-Net architecture for fully automated whole-breast segmentation based on diffusion-weighted imaging (DWI) and synthetic MRI (SyMRI) images.</p><p><strong>Methods: </strong>A total of 98 patients with 196 breasts were evaluated. All patients underwent 3.0T magnetic resonance (MR) examinations, which incorporated DWI and SyMRI techniques. The ground truth for breast segmentation was established through a manual, slice-by-slice approach performed by two experienced radiologists. The U-Net and nnU-Net deep learning algorithms were employed to segment the whole-breast. Performance was evaluated using various metrics, including the Dice Similarity Coefficient (DSC), accuracy, and Pearson's correlation coefficient.</p><p><strong>Results: </strong>For DWI and proton density (PD) of SyMRI, the nnU-Net outperformed the U-Net achieving the higher DSC in both the testing set (DWI, 0.930 ± 0.029 vs. 0.785 ± 0.161; PD, 0.969 ± 0.010 vs. 0.936 ± 0.018) and independent testing set (DWI, 0.953 ± 0.019 vs. 0.789 ± 0.148; PD, 0.976 ± 0.008 vs. 0.939 ± 0.018). The PD of SyMRI exhibited better performance than DWI, attaining the highest DSC and accuracy. The correlation coefficients R² for nnU-Net were 0.99 ~ 1.00 for DWI and PD, significantly surpassing the performance of U-Net.</p><p><strong>Conclusion: </strong>The nnU-Net exhibited exceptional segmentation performance for fully automated breast segmentation of contrast-free quantitative images. This method serves as an effective tool for processing large-scale clinical datasets and represents a significant advancement toward computer-aided quantitative analysis of breast DWI and SyMRI images.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"385"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173381","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":"Prevalence and morphometric characterization of the genial tubercle via CBCT: evidence from a Turkish population.","authors":"Berke Berberoglu, Nagihan Koç, Yagmur Zengin, Nihal Avcu","doi":"10.1186/s12880-025-01921-9","DOIUrl":"10.1186/s12880-025-01921-9","url":null,"abstract":"<p><strong>Background: </strong>The objective of this study was to assess the prevalence and morphometric characteristics of the genial tubercle (GT) in a Turkish population using cone-beam computed tomography (CBCT), by classifying the morphology of GTs and evaluating their width, height, and anatomical position, in relation to age and sex.</p><p><strong>Methods: </strong>A total of 356 CBCT images were collected from the radiology archive at Hacettepe University Faculty of Dentistry, involving 228 female and 128 male patients aged 18 years and older. The GTs were identified and classified using multiplanar reconstruction sections. Measurements taken included the width (GT-w) and height (GT-h) of the GTs, the distance from the GTs to the apex of the mandibular central incisors (I-SGT), the distance to the mandibular base (IGT-M), and the mandibular anterior thickness (MT).</p><p><strong>Results: </strong>The overall prevalence of GTs was found to be 90.4%, with 9.6% of patients showing no GTs present. The most frequently observed type was GT-3 (33.1%), while the least common was GT-5 (9.6%). A statistically significant relationship was found between GT types and sex, as well as age groups (p = 0.007 and p = 0.017, respectively). Measurements indicated that the GT-w, GT-h, I-SGT, and MT values for males were greater than those for females.</p><p><strong>Conclusions: </strong>The GTs exhibited significant variation in morphology according to age and sex, with GT-3 being the most common type and detectable GTs present in over 90% of individuals. Male participants demonstrated greater GT-w, GT-h, and MT values than females. The subdivision of Type 4 into 4 A and 4B provided a more detailed radiological characterization, which may serve as a useful reference in future anatomical and clinical research. CBCT images provide detailed information regarding the morphological assessment of GTs.</p><p><strong>Trial registration: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"381"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173530","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":"MedIENet: medical image enhancement network based on conditional latent diffusion model.","authors":"Weizhen Yuan, Yue Feng, Tiancai Wen, Guancong Luo, Jiexin Liang, Qianshuai Sun, Shufen Liang","doi":"10.1186/s12880-025-01909-5","DOIUrl":"10.1186/s12880-025-01909-5","url":null,"abstract":"<p><strong>Background: </strong>Deep learning necessitates a substantial amount of data, yet obtaining sufficient medical images is difficult due to concerns about patient privacy and high collection costs.</p><p><strong>Methods: </strong>To address this issue, we propose a conditional latent diffusion model-based medical image enhancement network, referred to as the Medical Image Enhancement Network (MedIENet). To meet the rigorous standards required for image generation in the medical imaging field, a multi-attention module is incorporated in the encoder of the denoising U-Net backbone. Additionally Rotary Position Embedding (RoPE) is integrated into the self-attention module to effectively capture positional information, while cross-attention is utilised to embed integrate class information into the diffusion process.</p><p><strong>Results: </strong>MedIENet is evaluated on three datasets: Chest CT-Scan images, Chest X-Ray Images (Pneumonia), and Tongue dataset. Compared to existing methods, MedIENet demonstrates superior performance in both fidelity and diversity of the generated images. Experimental results indicate that for downstream classification tasks using ResNet50, the Area Under the Receiver Operating Characteristic curve (AUROC) achieved with real data alone is 0.76 for the Chest CT-Scan images dataset, 0.87 for the Chest X-Ray Images (Pneumonia) dataset, and 0.78 for the Tongue Dataset. When using mixed data consisting of real data and generated data, the AUROC improves to 0.82, 0.94, and 0.82, respectively, reflecting increases of approximately 6%, 7%, and 4%.</p><p><strong>Conclusion: </strong>These findings indicate that the images generated by MedIENet can enhance the performance of downstream classification tasks, providing an effective solution to the scarcity of medical image training data.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"372"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173492","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":"Nomogram for predicting tumor-stroma ratio in pancreatic ductal adenocarcinoma using dual-energy computed tomography.","authors":"Weiyue Chen, Guihan Lin, Weibo Mao, Jingjing Cao, Shuiwei Xia, Min Xu, Chenying Lu, Minjiang Chen, Jiansong Ji","doi":"10.1186/s12880-025-01915-7","DOIUrl":"10.1186/s12880-025-01915-7","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to develop and validate a nomogram to predict both the tumor-stroma ratio (TSR) and the overall survival (OS) of patients with pancreatic ductal adenocarcinoma (PDAC) using preoperative dual-energy computed tomography (DECT) parameters.</p><p><strong>Methods: </strong>153 patients with histopathologically confirmed PDAC who underwent preoperative DECT scans were retrospectively reviewed and divided into high- and low-TSR groups based on histological analyses of surgical specimens. Several DECT parameters of the primary tumor were measured, including the normalized iodine concentration (NIC), effective atomic number, slope of the energy spectrum attenuation curve (K), CT values (40-100 keV), and extracellular volume fraction (ECVf), and analyzed alongside clinical and radiological data. Univariate and multivariate logistic regression models were used to identify independent predictors, which were then incorporated into radiology, DECT, and nomogram models. The association of the nomograms with OS was assessed using Kaplan-Meier curves and Cox regression analysis.</p><p><strong>Results: </strong>CT-reported lymph node status, NIC_venous, K_venous, and ECVf were identified as independent predictors of the TSR and included in the nomogram model. The nomogram demonstrated high predictive accuracy with an area under the receiver operating characteristic curve of 0.934 in the training set and 0.891 in the validation set, outperforming the radiology model (0.715 and 0.692, respectively). Patients with a high predicted TSR exhibited worse OS than those with a low predicted TSR.</p><p><strong>Conclusion: </strong>The DECT-based nomogram model provides a noninvasive and accurate preoperative prediction of the TSR and prognosis of patients with PDAC and may assist in individualized risk stratification and treatment planning.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"373"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173527","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}