Radiology advances最新文献

{"title":"Art of imaging: radiology evolution.","authors":"","doi":"10.1093/radadv/umag023","DOIUrl":"https://doi.org/10.1093/radadv/umag023","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 4","pages":"umag023"},"PeriodicalIF":1.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13338773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148407680","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":"AI-accelerated 3D gradient echo versus ultrashort echo time MRI for lung nodule detection and measurement.","authors":"Alexander W Marka, Kilian Weiss, Hannah Rosenkranz, Bernadette Scherer, Lisa Adams, Keno Bressem, Nicolas Lenhart, Tristan Lemke, Florian T Gassert, Marcus R Makowski, Daniela Pfeiffer, Gregor S Zimmermann, Seyer Safi, Dimitrios C Karampinos, Markus Graf, Sebastian Ziegelmayer","doi":"10.1093/radadv/umag029","DOIUrl":"10.1093/radadv/umag029","url":null,"abstract":"<p><strong>Background: </strong>Ultrashort echo time (UTE) sequences are widely used in pulmonary MRI due to favorable lung parenchyma signal but are limited by acquisition time and complex reconstruction.</p><p><strong>Purpose: </strong>To compare an AI-enhanced 3D gradient echo sequence with a UTE sequence for lung nodule detection and Lung-RADS classification, using CT as the reference standard.</p><p><strong>Materials and methods: </strong>In this single-center observational study, patients with at least one CT-detected lung nodule underwent pulmonary MRI. MRI included a respiratory-gated AI-accelerated 3D gradient-echo sequence (CSAI-GRE) and a respiratory-gated 3D UTE sequence, both acquired with comparable scan times. Three radiologists independently assessed image quality, nodule detection, and Lung-RADS classification. CT served as the reference standard. Statistical analyses included paired tests for image quality, mixed-effects modeling for lesion-level detection, and <i>κ</i> statistics for Lung-RADS agreement.</p><p><strong>Results: </strong>This study included 54 patients (mean age, 65 ± 12 years; 23 women [43%]) with 97 pulmonary nodules. Subjective image quality was higher for CSAI-GRE than UTE (mean score, 4.30 vs 3.91; <i>P</i> = .005). Lesion-level detection was 96.9% for CSAI-GRE and 92.8% for UTE; mixed-effects analysis showed higher conditional detection odds for CSAI-GRE (OR, 10.2; 95% CI, 2.4-43.3; <i>P</i> = .002). MRI-CT agreement for Lung-RADS categorization ranged from substantial to almost perfect across readers (<i>κ</i> range, 0.79-0.93 for CSAI-GRE; 0.72-0.94 for UTE). Lung-RADS reclassification relative to CT occurred in 12.3% (20/162; 95% CI, 8.1-18.3) of CSAI-GRE and 20.8% (33/159; 95% CI, 15.2-27.7) of UTE reader assessments, including 5.6% (9/162; 95% CI, 3.0%-10.2%) and 10.7% (17/159; 95% CI, 6.8%-16.5%) management-relevant changes, respectively.</p><p><strong>Conclusion: </strong>CSAI-GRE and UTE enabled reliable pulmonary nodule detection. CSAI-GRE provided superior subjective image quality and slightly higher detection performance than UTE. However, the Lung-RADS reclassification rates suggest that MRI-based Lung-RADS categorization may not yet be sufficiently robust for routine clinical application.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 4","pages":"umag029"},"PeriodicalIF":1.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13399443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148586625","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":"Multicenter AI- versus expert-assisted RECIST target lesion measurements in follow-up body CT of patients with cancer.","authors":"Max J J de Grauw, Max Westphal, Ewoud J Smit, Ernst Th Scholten, Tanja Loßau, Jan Moltz, Silvia Bottazzi, Renato Cuocolo, Anna D'Angelo, Ajo B George, Hugo C van Heusden, Adriano Liguori, Valentina Longo, Luigi Mannacio, Nguyen T N Minh, Ahmed E Othman, Andrea Ponsiglione, Joey Roosen, Maarten de Rooij, Luca Russo, Steven Schalekamp, Miranda Snoeren, Arnaldo Stanzione, Sebastian Steinmetz, Carlijn I Verkroost, Bastiaan Vernhout, Lina Xu, Derya Yakar, Matthieu J C M Rutten, Bram van Ginneken, Mathias Prokop, Alessa Hering","doi":"10.1093/radadv/umag028","DOIUrl":"10.1093/radadv/umag028","url":null,"abstract":"<p><strong>Background: </strong>Manual lesion measurements remain the standard for assessing oncologic treatment response, despite being time-consuming and prone to substantial interreader variability, which may lead to inconsistent response classification and subsequent variability in treatment decisions.</p><p><strong>Purpose: </strong>To evaluate the impact of an artificial intelligence (AI) system for assisted lesion measurement on reading time and measurement consistency in follow-up CT examinations using the Response Evaluation Criteria in Solid Tumors (RECIST 1.1).</p><p><strong>Methods and materials: </strong>In this retrospective reader study, follow-up chest-abdomen-pelvis CT examinations from 212 oncology patients collected at 2 Dutch hospitals were assessed by 23 readers (15 radiologists, 8 residents) recruited from 11 international institutions under 3 conditions: unassisted, AI-assisted, and expert-assisted (using a prior radiologist's unassisted measurement). To prevent bias related to the source of the measurements, readers were informed that all support was AI-derived. Primary outcomes were reading time to completion and interobserver measurement variability. For each outcome, a Bayesian generalized linear mixed model was used to analyze the results.</p><p><strong>Results: </strong>AI assistance significantly reduced per-patient reading time versus unassisted reading (-36.0 s; 95% CI, -53.0 to -22.1). At the lesion level, it was associated with a small increase in variability relative to the expert-derived reference standard (1.32 mm; 95% CI, 0.83-1.91). At the patient level, AI assistance did not meaningfully affect change in the sum of longest diameters (-0.38 mm; 95% CI, -1.57 to 0.72), but increased RECIST outcome agreement by 7.7% (95% CI, 2.8-12.7) compared with unassisted reading. Expert-assisted reading yielded even higher interreader agreement (13.3%; 95% CI, 8.6-18.1).</p><p><strong>Conclusion: </strong>AI assistance reduced reading time and improved patient-level RECIST agreement, despite a small increase in lesion-level measurement variability. These findings suggest that AI-assisted RECIST assessment may improve workflow and response classification consistency, while also providing a benchmark from expert-assisted reading for future AI development.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 5","pages":"umag028"},"PeriodicalIF":1.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13588574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009731","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":"Whole-body MRI, healthcare ethics and access equity: Where are we? Where are we going?","authors":"Ariadne Maria Krzyuy, Leonardo C Welling, Mário Augusto Cray da Costa","doi":"10.1093/radadv/umag027","DOIUrl":"https://doi.org/10.1093/radadv/umag027","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 4","pages":"umag027"},"PeriodicalIF":1.8,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13493186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148804519","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":"OpenMAP-BrainAge: generalizable and interpretable brain age predictor from MRI.","authors":"Pengyu Kan, Craig Jones, Kenichi Oishi","doi":"10.1093/radadv/umag025","DOIUrl":"10.1093/radadv/umag025","url":null,"abstract":"<p><strong>Background: </strong>Accurately estimating brain age can help identify deviations linked to neurodegenerative diseases, underscoring the need for robust models that accurately perform across heterogenous cohorts.</p><p><strong>Purpose: </strong>To develop an age prediction model that is interpretable and robust to demographic and technological variations in brain MRI.</p><p><strong>Materials and methods: </strong>We propose a transformer-based brain age model that analyzes 3D T1-weighted MRI. Model performance was assessed using mean absolute error (MAE). Associations between brain age gap (BAG, ie, predicted minus chronological age) and chronological age were evaluated in cognitive normal (CN) participants. Clinical relevance was assessed by examining BAG differences across cognitive groups and correlations with Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA).</p><p><strong>Results: </strong>We achieved an MAE 3.65 years on ADNI2 & 3 and OASIS3 test sets, and a high generalizability of MAE of 3.54 years on AIBL. In dementia, a notable increase in brain age gap (BAG) along with cognitive decline, with a mean of 0.15 years (95% CI: [-0.22, 0.51]) in CN, 2.55 years ([2.40, 2.70]) in mild cognitive impairment (MCI), and 6.12 years ([5.82, 6.43]) is noted. Negative correlation between BAG and cognitive scores was observed after adjustment for covariates, with <i>r</i> = -0.397 (<i>P</i> < 0.001) for MMSE and -0.393 (<i>P</i> < 0.001) for MoCA, where declining scores generally signify worsening cognitive performance. The saliency map highlighted white and deep gray matter structures as key regions influenced by brain aging.</p><p><strong>Conclusion: </strong>Our model effectively integrated multiview and volumetric information to achieve state-of-the-art brain age prediction, with improved generalizability, interpretability, and association with cognitive function.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 4","pages":"umag025"},"PeriodicalIF":1.8,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13387714/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148551964","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":"Standardizing attenuation across tube voltages and vertebral levels for opportunistic osteoporosis screening on low-dose chest CT.","authors":"Youngjune Kim, Sehyun Hong, Choong Guen Chee, Yusuhn Kang, Eugene Lee, Yeo Ju Kim, Joon Woo Lee","doi":"10.1093/radadv/umag026","DOIUrl":"10.1093/radadv/umag026","url":null,"abstract":"<p><strong>Background: </strong>Opportunistic screening of osteoporosis on CT has emerged as a cost-effective strategy for bone health assessment. However, vertebral attenuation varies substantially with CT tube voltage and spinal level, limiting standardization.</p><p><strong>Purpose: </strong>To model and standardize vertebral attenuation across tube voltages and spinal levels on low-dose chest CT for opportunistic osteoporosis screening.</p><p><strong>Materials and methods: </strong>This retrospective study included 589 patients (336 women; mean age ± standard deviation, 65.9 ± 12.4 years) who underwent 2 noncontrast low-dose chest CT examinations and 1 dual-energy X-ray absorptiometry within 3 months. Vertebral attenuation was measured at T10-L2 on CT using a deep learning-based software (AVIEW SpineBH, Coreline Soft). Osteoporosis was diagnosed using dual-energy X-ray absorptiometry. A linear mixed-effects model regressed attenuation on the logarithm of tube voltage, incorporating fixed effects for spinal level (T10-L2) and patient-specific random effects. Model performance was evaluated by converting the L1-120 kVp reference to other tube-voltage-level combinations and comparing predicted with observed attenuation using Pearson correlation, mean bias, and 95% limits of agreement. Diagnostic applicability was assessed by comparing receiver operating characteristic-derived thresholds with model-predicted thresholds converted from the reference.</p><p><strong>Results: </strong>Vertebral attenuation decreased by approximately 6-11 Hounsfield units (HU) for every 10-kVp increase (β = -88.8 HU per log[kVp]; <i>P</i> < .001) and declined by approximately 4-14 HU per level from T10 to L2. Predicted values closely matched observed measurements (<i>r</i> = 0.89; mean bias, 2.2 HU; 95% limits of agreement, -54 to 59 HU). The model-predicted thresholds showed strong agreement with receiver operating characteristic-derived thresholds, differing by 1-10 HU across tube voltages and spinal levels.</p><p><strong>Conclusions: </strong>A log-linear mixed-effects model enables population-level standardization of vertebral attenuation across tube voltages and spinal levels, supporting the development of more consistent opportunistic CT-based osteoporosis screening methods on noncontrast chest CT.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 3","pages":"umag026"},"PeriodicalIF":1.8,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13271096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148274544","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":"Correction to: Deep reinforcement learning for automatic anatomic CT landmark localization in Stanford Type B aortic dissection.","authors":"","doi":"10.1093/radadv/umag024","DOIUrl":"https://doi.org/10.1093/radadv/umag024","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/radadv/umag006.].</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 3","pages":"umag024"},"PeriodicalIF":0.0,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13189716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019952","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":"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":"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":1.8,"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":"Clinical benefits and current challenges of photon-counting detector CT in vascular imaging.","authors":"Ahmed O El Sadaney, Felix E Diehn, Prabhakar Shantha Rajiah, Ian Mark, Norbert G Campeau, Adam T Froemming, Francis I Baffour, Kelly K Horst, Lifeng Yu, Shuai Leng, Cynthia H McCollough, Joel G Fletcher","doi":"10.1093/radadv/umag021","DOIUrl":"10.1093/radadv/umag021","url":null,"abstract":"<p><p>Photon-counting detector CT (PCD-CT) directly detects individual photons and measures their energies, enabling the acquisition of multi-energy data from a single x-ray source with higher spatial resolution, thereby allowing visualization of smaller arteries and veins and other important imaging features not seen at conventional CT. It also substantially reduces calcium and stent blooming artifacts. Appropriate patient selection and task-based optimization of acquisition, reconstruction, and interpretation are critical to realize these benefits. Vascular imaging examinations for which early evidence shows that PCD-CT demonstrates higher diagnostic performance compared to conventional CT include coronary CT angiography (CTA) for evaluation of partially calcified plaques or stents, peripheral runoff CTA, detection of cerebrospinal fluid-venous fistulas, and head CTA for evaluation of small intracranial vessels and aneurysms. Current limitations include excessive image noise with the sharpest reconstruction kernels and challenges when adapting conventional CT protocols (eg, requiring sharper kernels, thinner slices, higher matrices, tailored scan modes) to achieve time-efficient image reconstruction and hanging protocols. New solutions and directions will include deep-learning-based image noise reduction, improved spectral separation, strategic triage of specific patient populations to PCD-CT systems, and subspecialty clinical practice recommendations for this new technology.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 3","pages":"umag021"},"PeriodicalIF":1.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13234556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148202048","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":"Rethinking radiology AI benchmarks.","authors":"Yi Lin, Yuzhe Yang, George Shih, Yifan Peng","doi":"10.1093/radadv/umag022","DOIUrl":"10.1093/radadv/umag022","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"3 3","pages":"umag022"},"PeriodicalIF":1.8,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13163161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147936812","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}