Spatially Regularized Super-Resolved Constrained Spherical Deconvolution (SR2-CSD) of diffusion MRI data

Motivation: Constrained Spherical Deconvolution (CSD) is a state-of-the-art method for estimating the fiber orientation distribution function (fODF) in white matter from diffusion MRI data. However, CSD faces limitations in resolving fiber crossings with small inter-fiber angles when using low spherical harmonic order and produces noisy fODFs when using high order. Goal(s): This study aims to improve the stability and angular resolution of fODFs from CSD. Approach: We extend the CSD estimation framework by including a spatial regularization term that promotes fiber continuity, using a J-invariant auto-calibrated total variation denoiser. Results: The proposed method enhances fiber crossing estimation and reduces spurious fibers. Impact: The improved stability of the proposed method enables the utilization of higher spherical harmonic orders, with a superior ability to solve complex fiber crossings. This work has the potential to increase the accuracy of fiber-tracking algorithms and brain connectivity estimations.