ArticleScientific reports2026
Deep learning-based automatic field of view planning for prostate MRI in oblique coronal and oblique axial planes.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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11 authors.
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Abstract
Accurate field-of-view (FoV) prescription in oblique coronal and axial planes is essential for high-quality prostate MRI but remains operator-dependent and variable. We developed and evaluated a ResNet-based deep learning framework for automated FoV planning. In this retrospective multicenter study, FoV prescriptions were annotated on PI-CAI dataset. Three readers assessed intra- and inter-rater variability to establish reference consistency. Three neural network variants were trained on 1,474 examinations from PI-CAI dataset (2012-2021), and the optimal model was selected by internal validation. Generalizability and clinical utility were tested on three external cohorts totaling 530 examinations (2021-2024) using a non-inferiority design. The selected model achieved non-inferior performance for slice positioning, with differences ranging from 0.16 ± 0.99 to 0.37 ± 0.48. Across sites, FoV overlaps ranged from 82.4 ± 4.1% to 88.7 ± 6.0%, and the angle differences between predicted and reference planes were 4.66 ± 4.89° (Site I), 3.46 ± 2.80° (Site II), and 2.99 ± 2.90° (Site III). Clinical utility was high at all sites, with acceptability rates of 97.9%, 97.7%,98.8%, 98.1% and 98.1% for Site I (Raters 1-5), 95.7%, 97.8%, 100%, 95.7% and 97.8% for Site II (Raters 1-5), and 100% for all raters at Site III. These findings demonstrate the feasibility of automated FoV positioning for prostate MRI and indicate excellent clinical utility.
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