We evaluate the benefits of shifting from a global white matter (WM) model to an adaptive (voxel-wise) model in the Generalized Richardson Lucy (GRL) framework. Using simulations, we show that GRL with an adaptive model (AGRL) could resolve crossing fiber configurations with heterogeneous properties, whereas conventional GRL did not. In in-vivo data, AGRL simultaneously used different deconvolution models. Compared to GRL, fiber orientation distributions of AGRL showed remarkable angular differences, especially for the second and third peak. Tractography with AGRL resulted in a more extensive reconstruction of the arcuate fasciculus, suggesting adaptive modelling as a promising future direction.
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