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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