Abstract #1716

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

Joel Fundaun¹, Valeria Oliva^1,2, Sandrine Bédard^1,3, Eddo Wesselink¹, Benjamin Lynn¹, Anoosha Pai S⁴, Dario Pfyffer¹, Merve Kaptan¹, Nazrawit Berhe¹, John Ratliff⁵, Serena Hu⁶, Zachary A. Smith⁷, Trevor J. Hastie⁸, Sean Mackey¹, Marnee McKay⁹, James M. Elliott^9,10, Scott L. Delp¹¹, Gary H. Glover¹², Akshay S. Chaudhari¹², Christine S. W. Law¹, Andrew C. Smith¹³, and Kenneth A. Weber II¹

¹Anesthesiology, Stanford University, Palo Alto, CA, United States, ²Italian National Health Institute, Rome, Italy, ³, Polytechnique Montréal, Montreal, QC, Canada, ⁴Department of Radiology, Stanford University, Palo Alto, CA, United States, ⁵Neurosurgery, Stanford University, Palo Alto, CA, United States, ⁶Orthopaedic Surgery, Stanford University, Palo Alto, CA, United States, ⁷Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States, ⁸Statistics, Stanford University, Palo Alto, CA, United States, ⁹Faculty of Medicine and Health, University of Sydney, Sydney, Australia, ¹⁰The Kolling Institute, Sydney, Australia, ¹¹Bioengineering and Mechanical Engineering, Stanford University, Palo Alto, CA, United States, ¹²Radiology, Stanford University, Palo Alto, CA, United States, ¹³Physical Medicine and Rehabilitation, University of Colorado, Aurora, CO, United States

Synopsis

Keywords: Analysis/Processing, Segmentation

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.

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