Radiology advances最新文献

{"title":"A 20-minute, free-running, whole-heart cardiac MRI to image myocardial injury following reperfused myocardial infarction: demonstration in a large animal model.","authors":"Xinheng Zhang, Hsin-Jung Yang, Yuheng Huang, Ghazal Yoosefian, Keyur Vora, Khalid Youssef, Benjamin Wilk, Anthony G Christodoulou, Debiao Li, Behzad Sharif, Frank S Prato, Andreas Kumar, Rohan Dharmakumar","doi":"10.1093/radadv/umag009","DOIUrl":"https://doi.org/10.1093/radadv/umag009","url":null,"abstract":"<p><strong>Background: </strong>Staging irreversible tissue injury in myocardial infarction (MI) enables risk assessment for post-MI major cardiovascular events. While cardiac MRI is the preferred modality for staging the severity of tissue injury, conventional scan protocols require long acquisition times with multiple breath-held and ECG-gated acquisitions, limiting its utilization.</p><p><strong>Purpose: </strong>To develop a free-breathing, whole-heart, non-ECG gated cardiac MRI for staging irreversible tissue injury in MI that can be completed in <20 minutes.</p><p><strong>Materials and methods: </strong>A fast cardiac MRI (Biograph, Siemens Healthcare, 3 T) method based on a low-rank tensor framework was developed (reconstruction performed in MATLAB) and tested against the conventional approach using a pre-clinical canine model of reperfused MI (<i>n</i> = 15) with histological validation. Each subject underwent 2 exams that were randomized 2 days apart (day 6-8 post MI, respectively). Correlations between the proposed and conventional methods and left-ventricular ejection fraction (LVEF), MI size and transmurality, size of microvascular obstruction (MVO), and intramyocardial hemorrhage (IMH) volumes were assessed using linear regression and Bland-Altman analysis.</p><p><strong>Results: </strong>Twelve out of 15 subjects survived the initial reperfusion injury. The proposed method reduced acquisition time by >50%. The cardiac MRI evidence of tissue injury was confirmed on histopathology in all cases. The agreements between the proposed and conventional methods for LVEF, MI volume, persistent MVO volume and IMH volume were excellent; limits of agreement (LoA) were -2.1%-1.8%, -2.9 <math><mi>%</mi></math> -3.3%, -2.4%-4.1%, and -1.5%-1.5%, respectively. MI transmurality and early MVO showed good agreement; LoA were -6.8%-9.7% and -6.6%-8.2%, respectively.</p><p><strong>Conclusion: </strong>The proposed free-breathing, whole-heart, non-ECG gated cardiac MRI approach permits accurate determination of tissue injury in a canine model with >2-fold reduction in scan time. While the method remains to be tested in patients, it has the potential to facilitate efficient use of cardiac MRI for staging the severity of tissue injury in patients with reperfused MI.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umag009"},"PeriodicalIF":0.0,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13099398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building trust and privacy in cross-border health data sharing for European cancer research.","authors":"Ricard Martínez Martínez, Ana de Marco, Ignacio Blanquer, Luis Martí-Bonmatí","doi":"10.1093/radadv/umag018","DOIUrl":"10.1093/radadv/umag018","url":null,"abstract":"<p><p>Data-driven research using artificial intelligence (AI) is transforming biomedical science, yet its application in medical imaging remains limited by fragmented datasets, heterogeneous legislation, and ethical uncertainties. The European Cancer Imaging Initiative (EUCAIM) addresses these barriers by establishing a federated, secure and interoperable European imaging infrastructure, fostering a trusted ecosystem for AI-enabled research. EUCAIM brings privacy, ethics, and security within a single, coherent operational framework. The project implements a risk-based, compliance-by-default approach that embeds Data Protection Impact Assessments throughout system design, translating legal requirements into verifiable technical safeguards. Its \"de facto\" anonymization model, aligned with the General Data Protection Regulation and Court of Justice jurisprudence, combines multi-stage anonymization pipelines, cryptographic hashing, and automated re-identification-risk analyses to deliver a federated Secure Processing Environment (SPE) for researchers. This federated infrastructure is consistent with the European Health Data Space Regulation (EHDSR) and national security frameworks, and ensures data sovereignty, interoperability, and accountability. A comprehensive governance and contractual framework, including Data Sharing and Transfer Agreements, clearly delineates roles and responsibilities, while the Data Access Committee provides robust ethical oversight. EUCAIM thus offers a lawful, secure, and sustainable model of a federated secure environment for the reuse of imaging data, advancing a genuinely data-driven research ecosystem.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umag018"},"PeriodicalIF":0.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13089403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147725340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved prostate diffusion imaging using deep learning denoising and phase correction with ultra-high-density coil array.","authors":"Sherry S Huang, Xinzeng Wang, Patricia Lan, Milica Medved, Nurullah Kaya, Clyve K Follante, Yunjeong Stickle, Jonathan Taylor, Ambereen Yousuf, Roger Engelmann, Fraser J L Robb, Arnaud Guidon, Grace Lee, Aytekin Oto","doi":"10.1093/radadv/umag019","DOIUrl":"https://doi.org/10.1093/radadv/umag019","url":null,"abstract":"<p><strong>Background: </strong>MR diffusion-weighted imaging (DWI), especially at high <i>b</i>-value, is a key acquisition to help identify clinically significant prostate cancer; however, it suffers from low signal-to-noise ratio (SNR), high noise floor, and susceptibility artifact.</p><p><strong>Purpose: </strong>To demonstrate the feasibility of improving DWI quality using a novel 50-channel pelvic coil in conjunction with a deep learning (DL)-based phase correction and a DL-denoising algorithm.</p><p><strong>Methods: </strong>In this prospective, single-center study, 24 consecutive men referred for prostate multiparametric MRI over 16 months were enrolled (age 47-79 years; mean, 68.1 years). Axial T2-weighted images and DWI were obtained using a prototype 50‑channel coil and standard clinical phased array (3 T Architect, GE HealthCare, USA). The DWI acquisitions were reconstructed with the vendor's deep learning denoising algorithm (ARDL). The same raw data were reconstructed offline using an investigational DL Phase Correction algorithm with ARDL (DLPC+ARDL). Two independent readers scored DWI and ADC series using 4 qualitative criteria. SNR and contrast-to-noise ratio (CNR) were measured on <i>b</i> = 1500 s/mm² images. Combined reader scores were compared using the Wilcoxon matched‑pairs signed‑rank test, inter‑reader variability was assessed using Cohen's κ, and quantitative SNR/CNR values were compared using 2‑tailed paired t‑tests.</p><p><strong>Results: </strong>Twenty men were analyzable for qualitative and 18 for quantitative metrics (reported as mean ± SD). 50‑channel pelvic coil with DLPC+ARDL produced the highest SNR (99.70 ± 28.50) and CNR (91.68 ± 44.39), exceeding 50‑channel with ARDL alone (SNR 56.44 ± 28.50; CNR 51.11 ± 28.67), 30‑channel anterior array with ARDL (SNR 31.41 ± 13.18; CNR 26.26 ± 13.52), and DLPC + ARDL (SNR 49.4 ± 18.6; CNR 44.7 ± 18.3) (all <i>P</i> < .0001). Reader scores favored DLPC+ARDL in prostate border definition, peripheral/transition zone distinction, lesion conspicuity, and confidence of extraprostatic extension (all <i>P</i> values for DWI at <i>b</i> = 1500 s/mm² < 0.0001; synthetic DWI at <i>b</i> = 2000 s/mm²: <i>P </i>= 7.5 × 10^-7-0.01). Inter‑reader agreement was fair for acquired DWI (quadratic‑weighted κ = 0.34) and lower for synthetic DWI (κ = 0.19).</p><p><strong>Conclusion: </strong>DWI images acquired using the 50‑channel pelvic coil and reconstructed with the DLPC + ARDL pipeline yield the highest image quality compared to ARDL only pipeline and to all 30-channel coil imaging.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umag019"},"PeriodicalIF":0.0,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13131225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147825586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis.","authors":"Po-Ting Chen, Dawei Chang, Yenjia Chen, Pochuan Wang, Andre Yanchen Yeh, Kao-Lang Liu, Ming-Shiang Wu, Wei-Chih Liao, Weichung Wang","doi":"10.1093/radadv/umag017","DOIUrl":"10.1093/radadv/umag017","url":null,"abstract":"<p><strong>Background: </strong>Pancreatic cancer (PC) is frequently missed on unenhanced CT examinations performed for unrelated clinical indications, where the pancreas is included incidentally and clinical suspicion is low.</p><p><strong>Purpose: </strong>To develop and validate a deep learning-based tool for PC diagnosis and risk stratification on unenhanced CT.</p><p><strong>Materials and methods: </strong>This retrospective study included 3080 unenhanced CT studies of Taiwanese patients with PC, other pancreatic diseases and normal pancreas between 2004 and 2019 from a tertiary hospital, randomly divided into training, validation, and internal test sets. Unenhanced CT studies from United States institutions were used for external testing. A hybrid convolutional neural network-transformer model was trained for PC diagnosis and risk stratification. Performance was evaluated using sensitivity, specificity, and area under the curve (AUC), with comparisons to 2 radiologists by McNemar's test and exploratory decision curve analysis.</p><p><strong>Results: </strong>The internal dataset included 713 PCs (mean age, 64.6 ± 12.0 years; 384 men), 1661 normal pancreas and 706 other pancreatic diseases. In an exploratory comparison restricted to unenhanced CT (29 PCs, 31 controls), the sensitivity of computer-aided diagnosis (CAD) tool (89.7%, 72.6-97.8) seemed comparable with that of 1 radiologist (86.2%, 68.3-96.1) and higher than another (41.4%, 23.5-61.1); but wide confidence intervals and inter-radiologist variability warrant cautious interpretation. In the internal test set (142 PCs, 474 controls), sensitivity was 90.8% (84.9-95.0) and specificity 93.0% (90.4-95.2) (AUC: 0.98), with sensitivity comparable to radiologist reports based on enhanced and unenhanced CT (95.4%, 90.2-98.3; <i>P</i> = .21). In the external set (42 PCs, 22 controls), sensitivity was 76.2% (60.5-87.9) and specificity 86.4% (65.1-97.1) (AUC: 0.89). The tool stratified cases into 7 risk levels with likelihood ratios ranging from <0.01 to 173.46. Exploratory decision curve analysis suggested potential net benefit across threshold probabilities.</p><p><strong>Conclusion: </strong>This tool may assist in the opportunistic detection and risk stratification of PC on unenhanced CT.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umag017"},"PeriodicalIF":0.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13092298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147731095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to implement a radiologist led whole-body MRI screening program.","authors":"Andrea S Kierans, Keith D Hentel, Katerina Dodelzon, George Shih, Martin R Prince, Joshua Lantos, Melissa K Frey, Ravi N Sharaf, Robert J Min, Preethi Guniganti","doi":"10.1093/radadv/umag014","DOIUrl":"10.1093/radadv/umag014","url":null,"abstract":"<p><p>Screening whole-body MRI (WB-MRI) is gaining increasing attention as a tool for early disease detection, with growing adoption driven largely by consumer demand and direct-to-consumer private platforms. While WB-MRI has demonstrated utility in high-risk populations, its use in asymptomatic individuals remains controversial due to concerns about low diagnostic yield, false positives, overdiagnosis, and the lack of survival outcome data. Despite these limitations, the popularity of WB-MRI is expected to rise given the aging population and aggressive marketing by direct-to-consumer companies, underscoring the need for thoughtful and proactive engagement by radiologists. Radiologists have an obligation to ensure that scientific rigor, ethical oversight, and multidisciplinary collaboration guide the expansion of WB-MRI. This review outlines the current evidence and evolving landscape of screening WB-MRI, describes the development and implementation of a program within an academic radiology practice, and discusses the downstream implications and cost-effectiveness of WB-MRI screening. As this technology continues to expand beyond traditional indications, radiologists must play a leading role in defining best practices and ensuring that implementation remains evidence-based, transparent, and patient-centered.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umag014"},"PeriodicalIF":0.0,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13008331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-enabled cardiac volumetry for opportunistic screening of cardiomegaly on chest CT: clinical validation with echocardiography.","authors":"Christopher M Fan, Angelo Scanio, Patricia Yokoo, Maya Wiessman, Michael Long, Matthew A Lewis, Yin Xi, Xinhui Duan, Roderick McColl, Suhny Abbara, Ronald Peshock, Fernando U Kay","doi":"10.1093/radadv/umag013","DOIUrl":"10.1093/radadv/umag013","url":null,"abstract":"<p><strong>Background: </strong>Cardiomegaly is a clinically significant incidental finding on chest computed tomography (CT) associated with heart failure, arrhythmias, and sudden cardiac death. Qualitative radiologist assessment is variable, and automated AI tools may enable objective opportunistic cardiac volumetry.</p><p><strong>Purpose: </strong>To evaluate whether AI-enabled total cardiac volume (TCV_AI) derived from non-ECG-gated, non-contrast chest CT can identify cardiomegaly as defined by echocardiography.</p><p><strong>Materials and methods: </strong>This retrospective study included 307 consecutive patients (median age, 67 years; 56% male) who underwent non-contrast chest CT at a single center on 7 scanner types (4 vendors) and clinically indicated echocardiography within 31 days. A commercially available AI tool (AI-Rad Companion, Siemens Healthineers) automatically quantified TCV_AI, indexed to body surface area (TCV_AI/BSA). Echocardiography reports were reviewed for chamber dilation and left ventricular hypertrophy (LVH), collectively defined as cardiomegaly. Associations between TCV_AI/BSA and echocardiographic findings were assessed using correlation, ordinal regression, and receiver operating characteristic (ROC). Interscan repeatability was evaluated in 248 patients with 544 repeat CT examinations. Prespecified sex-specific thresholds were tested in a temporally independent validation cohort of 50 patients.</p><p><strong>Results: </strong>Median TCV_AI was higher in patients with cardiomegaly than those without (1061.9 vs 798.4 mL; <i>P</i> < .001). TCV_AI/BSA was associated with chamber dilation and LVH severity on univariate analysis and remained associated in multivariable ordinal models, except for right ventricular dilation. Discriminatory performance was fair to good, with area under the curve (AUC) 0.81 (95% CI, 0.75-0.87) in men and 0.77 (95% CI, 0.69-0.85) in women. Interscan repeatability was excellent (intraclass correlation coefficient [ICC]: 0.93). In independent validation, performance ranged from sensitivity 89.3%/specificity 27.3% at a high-sensitivity threshold to sensitivity 28.6%/specificity 100% at a high-specificity threshold.</p><p><strong>Conclusion: </strong>AI-derived cardiac volume from routine chest CT shows fair to good performance for identifying echocardiography-defined cardiomegaly with high measurement repeatability, supporting a potential role for automated cardiac volumetry as an objective, opportunistic biomarker.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umag013"},"PeriodicalIF":0.0,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13016890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147523210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Art of imaging: brain sagging in spinal cerebrospinal fluid leak.","authors":"Parnian Habibi","doi":"10.1093/radadv/umaf041","DOIUrl":"10.1093/radadv/umaf041","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umaf041"},"PeriodicalIF":0.0,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12965401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Art of imaging: Aurora Cerebralis Rivers of the Mind.","authors":"","doi":"10.1093/radadv/umag012","DOIUrl":"https://doi.org/10.1093/radadv/umag012","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umag012"},"PeriodicalIF":0.0,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12975340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147446394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to editorial \"From Probability to Practice: Demystifying When Lung Shunt Fraction Matters in ⁹⁰Y Selective Internal Radiation Therapy\".","authors":"M Allan Thomas, Christopher D Malone","doi":"10.1093/radadv/umag010","DOIUrl":"https://doi.org/10.1093/radadv/umag010","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umag010"},"PeriodicalIF":0.0,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13050606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From probability to practice: demystifying when lung shunt fraction matters in ⁹⁰Y selective internal radiation therapy.","authors":"Ningcheng Peter Li, Shabaz Khan, Tomas Figueira, Neil J Resnick","doi":"10.1093/radadv/umag011","DOIUrl":"https://doi.org/10.1093/radadv/umag011","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 2","pages":"umag011"},"PeriodicalIF":0.0,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13050607/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}