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

Anton Sheahan Quinsten; Simon Joshua Hornisch; Marcel Gratz; Mathias Holtkamp; Michael Forsting; Kai Nassenstein; Lale Umutlu; Armin Lühr; Jens Kleesiek; Moon-Sung Kim; Aydin Demircioğlu

doi:10.1016/j.jmir.2026.102197

February 12, 2026Journal of medical imaging and radiation sciencesOpen Access

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

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Key Points

This research aims to enhance shoulder MRI by using deep learning for automatic field-of-view planning.
Developed a deep learning algorithm for FOV planning in shoulder MRI.
Tested the algorithm across different institutions to assess generalizability.
Compared FOV outcomes with those determined by professional radiographers.
Achieved performance comparable to radiographers in field-of-view planning.
Demonstrated effectiveness across various institutions.
Showed high clinical utility for practical application.

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Implication

Automated deep learning-based FOV prescription achieves performance comparable to radiographers, indicating high clinical utility.

Abstract

DL-based automated FOV prescription for shoulder MRI achieves performance comparable to radiographers, generalizes across institutions, and demonstrates high clinical utility.

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Cite This Study

Quinsten et al. (2026) studied this question.

synapsesocial.com/papers/6992b3769b75e639e9b08288 https://doi.org/10.1016/j.jmir.2026.102197

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

Key Points

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Implication

Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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