Radiology-Artificial Intelligence最新文献

{"title":"Erratum for: CMRxRecon2024: A Multimodality, Multiview k-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI.","authors":"Zi Wang, Fanwen Wang, Chen Qin, Jun Lyu, Cheng Ouyang, Shuo Wang, Yan Li, Mengyao Yu, Haoyu Zhang, Kunyuan Guo, Zhang Shi, Qirong Li, Ziqiang Xu, Yajing Zhang, Hao Li, Sha Hua, Binghua Chen, Longyu Sun, Mengting Sun, Qing Li, Ying-Hua Chu, Wenjia Bai, Jing Qin, Xiahai Zhuang, Claudia Prieto, Alistair Young, Michael Markl, He Wang, Lian-Ming Wu, Guang Yang, Xiaobo Qu, Chengyan Wang","doi":"10.1148/ryai.259001","DOIUrl":"10.1148/ryai.259001","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 2","pages":"e259001"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658892","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":"Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images.","authors":"Daniel C Mann, Michael W Rutherford, Phillip Farmer, Joshua M Eichhorn, Fathima Fijula Palot Manzil, Christopher P Wardell","doi":"10.1148/ryai.240050","DOIUrl":"10.1148/ryai.240050","url":null,"abstract":"<p><p>Purpose To construct and evaluate the performance of a machine learning model for bone segmentation using whole-body CT images. Materials and Methods In this retrospective study, whole-body CT scans (from June 2010 to January 2018) from 90 patients (mean age, 61 years ± 9 [SD]; 45 male, 45 female) with multiple myeloma were manually segmented using 60 labels and subsegmented into cortical and trabecular bone. Segmentations were verified by board-certified radiology and nuclear medicine physicians. The impacts of isotropy, resolution, multiple labeling schemes, and postprocessing were assessed. Model performance was assessed on internal and external test datasets (362 scans) and benchmarked against the TotalSegmentator segmentation model. Performance was assessed using Dice similarity coefficient (DSC), normalized surface distance (NSD), and manual inspection. Results Skellytour achieved consistently high segmentation performance on the internal dataset (DSC: 0.94, NSD: 0.99) and two external datasets (DSC: 0.94, 0.96; NSD: 0.999, 1.0), outperforming TotalSegmentator on the first two datasets. Subsegmentation performance was also high (DSC: 0.95, NSD: 0.995). Skellytour produced finely detailed segmentations, even in low-density bones. Conclusion The study demonstrates that Skellytour is an accurate and generalizable bone segmentation and subsegmentation model for CT data; it is available as a Python package via GitHub <i>(https://github.com/cpwardell/Skellytour)</i>. <b>Keywords:</b> CT, Informatics, Skeletal-Axial, Demineralization-Bone, Comparative Studies, Segmentation, Supervised Learning, Convolutional Neural Network (CNN) <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license. See also commentary by Khosravi and Rouzrokh in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240050"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450334","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":"Bone Appetit: Skellytour Sets the Table for Robust Skeletal Segmentation.","authors":"Bardia Khosravi, Pouria Rouzrokh","doi":"10.1148/ryai.250057","DOIUrl":"10.1148/ryai.250057","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 2","pages":"e250057"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143658888","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":"Editor's Recognition Awards.","authors":"Charles E Kahn","doi":"10.1148/ryai.250164","DOIUrl":"https://doi.org/10.1148/ryai.250164","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 2","pages":"e250164"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.","authors":"Alexander S Giuffrida, Sulaiman Sheriff, Vicki Huang, Brent D Weinberg, Lee A D Cooper, Yuan Liu, Brian J Soher, Michael Treadway, Andrew A Maudsley, Hyunsuk Shim","doi":"10.1148/ryai.230579","DOIUrl":"10.1148/ryai.230579","url":null,"abstract":"<p><p>Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/generalized autocalibrating partial parallel acquisition scans from clinical trials for glioblastoma (trial 1, May 2014-October 2018) and major depressive disorder (trial 2, 2022-2023). The training dataset included 685 000 spectra from 20 participants (60 scans) in trial 1. The testing dataset included 115 000 spectra from five participants (13 scans) in trial 1 and 145 000 spectra from seven participants (16 scans) in trial 2. A comparative analysis was performed between NNFit and a widely used parametric-modeling spectral quantitation method (FITT). Metabolite maps generated by each method were compared using the structural similarity index measure (SSIM) and linear correlation coefficient (<i>R²</i>). Radiation treatment volumes for glioblastoma based on metabolite maps were compared using the Dice coefficient and a two-tailed <i>t</i> test. Results Mean SSIMs and <i>R</i>² values for trial 1 test set data were 0.91 and 0.90 for choline, 0.93 and 0.93 for creatine, 0.93 and 0.93 for <i>N</i>-acetylaspartate, 0.80 and 0.72 for myo-inositol, and 0.59 and 0.47 for glutamate plus glutamine. Mean values for trial 2 test set data were 0.95 and 0.95, 0.98 and 0.97, 0.98 and 0.98, 0.92 and 0.92, and 0.79 and 0.81, respectively. The treatment volumes had a mean Dice coefficient of 0.92. The mean processing times were 90.1 seconds for NNFit and 52.9 minutes for FITT. Conclusion A deep learning approach to spectral quantitation offers performance similar to that of conventional quantification methods for EPSI data, but with faster processing at short TE. <b>Keywords:</b> MR Spectroscopy, Neural Networks, Brain/Brain Stem <i>Supplemental material is available for this article</i>. © RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230579"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984891","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":"Deep Learning-based Brain Age Prediction Using MRI to Identify Fetuses with Cerebral Ventriculomegaly.","authors":"Hyuk Jin Yun, Han-Jui Lee, Sungmin You, Joo Young Lee, Jerjes Aguirre-Chavez, Lana Vasung, Hyun Ju Lee, Tomo Tarui, Henry A Feldman, P Ellen Grant, Kiho Im","doi":"10.1148/ryai.240115","DOIUrl":"10.1148/ryai.240115","url":null,"abstract":"<p><p>Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impaired neurodevelopmental outcomes. Recently, a novel fetal brain age prediction method using a two-dimensional (2D) single-channel convolutional neural network (CNN) with multiplanar MRI sections showed the potential to detect fetuses with VM. This study examines the diagnostic performance of a deep learning-based fetal brain age prediction model to distinguish fetuses with VM (<i>n</i> = 317) from typically developing fetuses (<i>n</i> = 183), the severity of VM, and the presence of associated CNS abnormalities. The predicted age difference (PAD) was measured by subtracting the predicted brain age from the gestational age in fetuses with VM and typical development. PAD and absolute value of PAD (AAD) were compared between VM and typically developing fetuses. In addition, PAD and AAD were compared between subgroups by VM severity and the presence of associated CNS abnormalities in VM. Fetuses with VM showed significantly larger AAD than typically developing fetuses (<i>P</i> < .001), and fetuses with severe VM showed larger AAD than those with moderate VM (<i>P</i> = .004). Fetuses with VM and associated CNS abnormalities had significantly lower PAD than fetuses with isolated VM (<i>P</i> = .005). These findings suggest that fetal brain age prediction using the 2D single-channel CNN method has the clinical ability to assist in identifying not only the enlargement of the ventricles but also the presence of associated CNS abnormalities. <b>Keywords:</b> MR-Fetal (Fetal MRI), Brain/Brain Stem, Fetus, Supervised Learning, Machine Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms <i>Supplemental material is available for this article.</i> ©RSNA, 2025.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240115"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450327","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":"CMRxRecon2024: A Multimodality, Multiview k-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI.","authors":"Zi Wang, Fanwen Wang, Chen Qin, Jun Lyu, Cheng Ouyang, Shuo Wang, Yan Li, Mengyao Yu, Haoyu Zhang, Kunyuan Guo, Zhang Shi, Qirong Li, Ziqiang Xu, Yajing Zhang, Hao Li, Sha Hua, Binghua Chen, Longyu Sun, Mengting Sun, Qing Li, Ying-Hua Chu, Wenjia Bai, Jing Qin, Xiahai Zhuang, Claudia Prieto, Alistair Young, Michael Markl, He Wang, Lian-Ming Wu, Guang Yang, Xiaobo Qu, Chengyan Wang","doi":"10.1148/ryai.240443","DOIUrl":"10.1148/ryai.240443","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e240443"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143060372","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":"Accelerating Complex Tissue Analysis in Prostate MRI: From Hours to Seconds Using Physics-informed Neural Networks.","authors":"Lisa C Adams, Keno K Bressem","doi":"10.1148/ryai.250016","DOIUrl":"https://doi.org/10.1148/ryai.250016","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 2","pages":"e250016"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the Trust Gap: Conformal Prediction for AI-based Intracranial Hemorrhage Detection.","authors":"Peter K Ngum, Christopher G Filippi","doi":"10.1148/ryai.250032","DOIUrl":"https://doi.org/10.1148/ryai.250032","url":null,"abstract":"","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":"7 2","pages":"e250032"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules.","authors":"Thomas Z Li, Kaiwen Xu, Aravind Krishnan, Riqiang Gao, Michael N Kammer, Sanja Antic, David Xiao, Michael Knight, Yency Martinez, Rafael Paez, Robert J Lentz, Stephen Deppen, Eric L Grogan, Thomas A Lasko, Kim L Sandler, Fabien Maldonado, Bennett A Landman","doi":"10.1148/ryai.230506","DOIUrl":"10.1148/ryai.230506","url":null,"abstract":"<p><p>Purpose To evaluate the performance of eight lung cancer prediction models on patient cohorts with screening-detected, incidentally detected, and bronchoscopically biopsied pulmonary nodules. Materials and Methods This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose CT, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. The area under the receiver operating characteristic curve of eight validated models, including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence (AI) on chest CT scans, longitudinal imaging AI, and multimodal approaches for prediction of lung cancer risk was assessed in nine cohorts (<i>n</i> = 898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions. Each model was implemented from their published literature, and each cohort was curated from primary data sources collected over periods from 2002 to 2021. Results No single predictive model emerged as the highest-performing model across all cohorts, but certain models performed better in specific clinical contexts. Single-time-point chest CT AI performed well for screening-detected nodules but did not generalize well to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively good performance on incidentally detected nodules. When applied to biopsied nodules, all models showed low performance. Conclusion Eight lung cancer prediction models failed to generalize well across clinical settings and sites outside of their training distributions. <b>Keywords:</b> Diagnosis, Classification, Application Domain, Lung <i>Supplemental material is available for this article.</i> © RSNA, 2025 See also commentary by Shao and Niu in this issue.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e230506"},"PeriodicalIF":8.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11950892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143190741","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}