Microstructural Parameter Estimation of Skeletal Muscle Using Random Forest Model of dMRI
Noel M. Naughton1, Nicolas R. Gallo2, Aaron T. Anderson3,4, and John J. Georgiadis1,2
1Mechanical Science and Engineering, University of Illinois at Urbana Champaign, Urbana, IL, United States, 2Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States, 3Beckman Institute for Advanced Science and Technology, Univeristy of Illinois at Urbana Champaign, Urbana, IL, United States, 4Radiology, Carle Foundation Hospital, Urbana, IL, United States
Results are presented for a random forest model to estimate skeletal muscle microstructure parameters from dMRI data. The model exhibits the ability to estimate microstructural parameters from both numerically simulated and experimentally acquired dMRI data suggesting that random forests, and machine learning more generally, may be a useful tool in dMRI microstructure estimation of skeletal muscle.
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