Deep learning-based automatic field of view planning for prostate MRI in oblique coronal and oblique axial planes

Clinical Snapshot

Key Findings

• P Value: Not explicitly reported for primary outcomes

• Effect Size: FoV overlap 82.4-88.7%, angle differences 2.99-4.66°

• Primary Outcome: FoV positioning accuracy and clinical acceptability

• Nnt Or Sensitivity: Clinical acceptability rates 95.7-100% across all sites and raters

• Confidence Interval: Slice positioning differences 0.16 ± 0.99 to 0.37 ± 0.48

Clinical Application

High feasibility given demonstrated performance across multiple centers with existing MRI infrastructure Relevant to Australian radiology practices, potentially reducing operator variability and improving standardization across public and private imaging centers. May support Medicare Benefits Schedule optimization for prostate MRI Male patients requiring prostate MRI across radiology departments

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.

References

1. 1.Quinsten, A., Sheahan, A., Wetter, A., Raczkowski, M., Trembecki, Ł., Guz, T., Oliveira, S., Buchkremer, R., Matusiewicz, D., Nassenstein, K., Forsting, M., & Demircioğlu, A. (2026). Deep learning-based automatic field of view planning for prostate MRI in oblique coronal and oblique axial planes. Scientific Reports. https://doi.org/10.1016/j.ejrad.2020.109384