Predicted effects of image acquisition and analysis conditions on DTMRI tractography–based muscle architecture estimates

Abstract

AbstractPurposeTo quantify the effects of the intrinsic signal pattern, image acquisition conditions, and data analysis conditions on diffusion‐tensor MRI (DTMRI) tractography–based muscle architecture estimates using a sampling–reconstruction assessment framework.MethodsNumerical models of muscles were constructed with realistic architectural properties. DTMRI signals were computed at signal‐to‐noise ratio (SNR) of 24–96 and common voxel sizes. Fiber tracking was performed, and the results were compared with the known architectural properties.ResultsSNR exerted the most significant impact on the outcome. The outcome variables approached asymptotes at SNR ≈ 54. Large in‐plane voxel dimensions reduced the similarity between reconstructed fibers and the known architectural properties. Higher order polynomials helped reconstruct fibers with more complicated geometry but overfit noise for less complex geometries. The intrinsic fiber curvature also affected the robustness of polynomial smoothing to SNR. Other conditions, such as the fiber dimensionality, voxel aspect ratio, and slice thickness, did not affect the outcomes.ConclusionSNR ≥ 54 is recommended for accurate muscle architecture characterization using DTMRI. Averaged across all simulated conditions, the greatest percent errors under SNR = 54 were −5.6% and −4.0% for the pennation angle and fiber‐tract length estimates, respectively. For fiber tracts with intermediate intrinsic curvature, the greatest percent error for the curvature estimate was 9.8% for SNR = 54. Smaller in‐plane voxel size (≤1.5 mm) is preferred to minimize the estimation error in architectural properties. If necessary, slice thickness may be adjusted within typical ranges to achieve sufficient SNR when slices are aligned near the fiber direction. Third‐order polynomial fitting is appropriate for smoothing fiber tracts.