Sara Neves Silva, Tomas Woodgate, Sarah McElroy, Michela Cleri, Kamilah St Clair, Jordina Aviles Verdera, Kelly Payette, Alena Uus, Lisa Story, David Lloyd, Mary A Rutherford, Joseph V Hajnal, Kuberan Pushparajah, Jana Hutter
{"title":"AutOmatic floW planning for fetaL MRI (OWL).","authors":"Sara Neves Silva, Tomas Woodgate, Sarah McElroy, Michela Cleri, Kamilah St Clair, Jordina Aviles Verdera, Kelly Payette, Alena Uus, Lisa Story, David Lloyd, Mary A Rutherford, Joseph V Hajnal, Kuberan Pushparajah, Jana Hutter","doi":"10.1016/j.jocmr.2025.101888","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Widening access to fetal flow imaging by automating real-time planning of 2D phase-contrast flow imaging (OWL).</p><p><strong>Methods: </strong>Two subsequent deep learning networks for fetal body localization and cardiac landmark detection on a coronal whole-uterus scan were trained on 167 and 71 fetal datasets, respectively, and implemented for real-time automatic planning of phase-contrast sequences. OWL was evaluated retrospectively in 10 datasets and prospectively in 7 fetal subjects (36+3-39+3 gestational weeks), with qualitative and quantitative comparisons to manual planning.</p><p><strong>Results: </strong>OWL was successfully implemented in 6/7 prospective cases. Fetal body localization achieved a Dice score of 0.94 ± 0.05, and cardiac landmark detection accuracies were 5.77 ± 2.91 mm (descending aorta), 4.32 ± 2.44 mm (spine), and 4.94 ± 3.82 mm (umbilical vein). Planning quality was 2.73/4 (automatic) and 3.0/4 (manual). Indexed flow measurements differed by - 1.8% (range - 14.2% to 14.9%) between OWL and manual planning.</p><p><strong>Conclusions: </strong>OWL achieved real-time automated planning of 2D phase-contrast MRI for 2 major vessels, demonstrating feasibility at 0.55T with potential generalisation across field strengths, extending access to this modality beyond specialised centres.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":" ","pages":"101888"},"PeriodicalIF":4.2000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cardiovascular Magnetic Resonance","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.jocmr.2025.101888","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Purpose: Widening access to fetal flow imaging by automating real-time planning of 2D phase-contrast flow imaging (OWL).
Methods: Two subsequent deep learning networks for fetal body localization and cardiac landmark detection on a coronal whole-uterus scan were trained on 167 and 71 fetal datasets, respectively, and implemented for real-time automatic planning of phase-contrast sequences. OWL was evaluated retrospectively in 10 datasets and prospectively in 7 fetal subjects (36+3-39+3 gestational weeks), with qualitative and quantitative comparisons to manual planning.
Results: OWL was successfully implemented in 6/7 prospective cases. Fetal body localization achieved a Dice score of 0.94 ± 0.05, and cardiac landmark detection accuracies were 5.77 ± 2.91 mm (descending aorta), 4.32 ± 2.44 mm (spine), and 4.94 ± 3.82 mm (umbilical vein). Planning quality was 2.73/4 (automatic) and 3.0/4 (manual). Indexed flow measurements differed by - 1.8% (range - 14.2% to 14.9%) between OWL and manual planning.
Conclusions: OWL achieved real-time automated planning of 2D phase-contrast MRI for 2 major vessels, demonstrating feasibility at 0.55T with potential generalisation across field strengths, extending access to this modality beyond specialised centres.
期刊介绍:
Journal of Cardiovascular Magnetic Resonance (JCMR) publishes high-quality articles on all aspects of basic, translational and clinical research on the design, development, manufacture, and evaluation of cardiovascular magnetic resonance (CMR) methods applied to the cardiovascular system. Topical areas include, but are not limited to:
New applications of magnetic resonance to improve the diagnostic strategies, risk stratification, characterization and management of diseases affecting the cardiovascular system.
New methods to enhance or accelerate image acquisition and data analysis.
Results of multicenter, or larger single-center studies that provide insight into the utility of CMR.
Basic biological perceptions derived by CMR methods.