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Abstract #3392

Effect of Acquisition and Analysis Conditions on Accuracy of DTI-Based Muscle Architecture Estimates: Predictions Using Numerical Simulations

Xingyu Zhou1,2, Carly Lockard2,3, Melissa Hooijmans4, and Bruce Damon2,3
1Biomedical Engineering, Vanderbilt University, Nashvill, TN, United States, 2Carle Clinical Imaging Research Program, Carle Health, Urbana, IL, United States, 3Stephens Family Clinical Research Institute, Carle Health, Urbana, IL, United States, 4Amsterdam University Medical Center, Amsterdam, Netherlands


Diffusion-tensor imaging (DTI) based fiber tractography is a useful tool to study the architecture of human skeletal muscle. However, effects of image acquisition and analysis conditions on the outcome of architectural estimates are challenging to examine in vivo. In this work, we describe a numerical simulation framework where the ground truth of muscle architecture is known and the outcome can be tested under different conditions. Results show that the estimate of fiber curvature is most affected by image noise. While second-order polynomial fitting of fiber tracts is more robust to image noise, third-order fitting performs better on highly curved fibers.

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