Automated MRI-Based Quantification of Forearm Muscle Health and Associations with Hand Function

Motivation: Hand function is impaired in many conditions. MRI-derived muscle health markers may improve the evaluation of conditions affecting hand function. Traditional manual segmentation is time-consuming, necessitating automated approaches. Goal(s): Develop an accurate method to automatically assess forearm muscle health (muscle volume, intramuscular fat) and assess their relationship to hand function. Approach: We developed and tested a computer-vision model for automated forearm segmentation using fat-water MRI, then assessed associations between muscle health (volume, intramuscular fat) and hand function (grip strength, dexterity). Results: The computer-vision model achieved high accuracy and good-excellent reliability. Muscle volume was associated with BMI and grip strength. Impact: We developed an accurate, reliable computer-vision model to automatically segment forearm muscles, which will be made openly available. This method can improve clinical assessment of forearm muscle health leading to more efficient evaluation and management of conditions affecting hand function.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1716
Primary Topic
Infrared Thermography in Medicine
Type
article
Field-Weighted Citation Impact
0.00
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article

Automated MRI-Based Quantification of Forearm Muscle Health and Associations with Hand Function

Sean Mackey, Gary H. Glover, Lynn Bry, Valeria Oliva et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Infrared Thermography in Medicine
article

Automated MRI-Based Quantification of Forearm Muscle Health and Associations with Hand Function

Sean Mackey, Gary H. Glover, Lynn Bry, Valeria Oliva, Dario Pfyffer, Christine Law, Merve Kaptan, Kenneth A. Weber, Sandrine Bédard, Scott L. Delp, Trevor Hastie, Zachary A. Smith, Andrew C. Smith, Marnee J. McKay, John K. Ratliff, Evert Onno Wesselink, Serena S. Hu, Akshay Sanjay Chaudhari, Joel Fundaun, Anoosha Pai S, Nazrawit Berhe, James M. Elliott
article en

Abstract

Motivation: Hand function is impaired in many conditions. MRI-derived muscle health markers may improve the evaluation of conditions affecting hand function. Traditional manual segmentation is time-consuming, necessitating automated approaches. Goal(s): Develop an accurate method to automatically assess forearm muscle health (muscle volume, intramuscular fat) and assess their relationship to hand function. Approach: We developed and tested a computer-vision model for automated forearm segmentation using fat-water MRI, then assessed associations between muscle health (volume, intramuscular fat) and hand function (grip strength, dexterity). Results: The computer-vision model achieved high accuracy and good-excellent reliability. Muscle volume was associated with BMI and grip strength. Impact: We developed an accurate, reliable computer-vision model to automatically segment forearm muscles, which will be made openly available. This method can improve clinical assessment of forearm muscle health leading to more efficient evaluation and management of conditions affecting hand function.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 65%
Infrared Thermography in Medicine
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