Automatic field-of-view planning for magnetic resonance shoulder imaging using Deep Learning

Clinical Snapshot

Key Findings

• P Value: Not reported for primary comparisons

• Effect Size: IoU: 0.847±0.003 (coronal), 0.852±0.007 (sagittal)

• Primary Outcome: Automated FOV prescription performance metrics

• Nnt Or Sensitivity: Clinical utility rating: 97.2% acceptable prescriptions

• Confidence Interval: MASD: 1.016±0.153 slices, MAAD: 3.259±0.190°

Clinical Application

High - technology integrable into existing MRI workflows with standard equipment Highly relevant to Australian radiology departments seeking workflow optimization and standardization, aligns with RANZCR quality improvement initiatives Patients requiring shoulder MRI across diverse healthcare settings

Abstract

INTRODUCTION: Accurate prescription of oblique coronal and oblique sagittal field of views (FOV) is essential for diagnostic shoulder MRI. Manual planning is radiographer-dependent, time-consuming, and subject to inter- and intra-operator variability, leading to inconsistent image quality and incomplete coverage. Although deep learning (DL) has advanced automated scan planning in non-oblique planes, oblique shoulder prescriptions remain underexplored; an automated DL approach could standardize FOV prescription, reduce operator dependence, and improve reproducibility and workflow without compromising diagnostic quality. METHODS: In this retrospective multicenter study, 575 shoulder MRI examinations (2019-2025) from four sites were included. Sites A (n=151) and B (n=220) were used for training; testing was performed on sites C (n=61), and D (n=143). A two-stage pipeline was implemented using five oriented bounding box (OBB) variants of YOLOv11 (n, s, m, l, x): Stage 1 performed slice selection; Stage 2 performed FOV prescription. Performance was evaluated against radiographers' prescriptions using mean absolute slice difference (MASD, slices), intersection over union (IoU), and mean absolute angle difference (MAAD, degrees). Clinical utility was assessed by three raters. RESULTS: The YOLOv11-OBB-l model achieved the lowest MASD for Stage 1 (1.016±0.153 slices). For Stage 2, YOLOv11-OBB-x performed best (coronal IoU, 0.847±0.003; sagittal IoU, 0.852±0.007; MAAD, 3.259±0.190°). During testing across each site, MASD ranged from 0.700±0.837 to 1.192±2.550 slices; MAAD from 2.811±2.348 to 4.396±7.158°; coronal IoU from 0.800±0.092 to 0.872±0.065; and sagittal IoU from 0.824±0.111 to 0.887±0.047. Mean clinical utility was 97.2%. Performance was noninferior to interrater variability across all sites and metrics. CONCLUSION: DL-based automated FOV prescription for shoulder MRI achieves performance comparable to radiographers, generalizes across institutions, and demonstrates high clinical utility.

References

1. 1.Quinsten, A., Sheahan, A. H., Hornisch, S. J., Gratz, M., Holtkamp, M., Forsting, M., Nassenstein, K., Umutlu, L., Lühr, A., Kleesiek, J., Kim, M.-S., & Demircioğlu, A. (2026). Automatic field-of-view planning for magnetic resonance shoulder imaging using Deep Learning. Journal of Medical Imaging and Radiation Sciences. https://doi.org/10.1016/j.jmir.2026.102197