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Abstract #0876

Resolving heterogeneous crossing fibers with Adaptive modelling and Generalized Richardson Lucy spherical deconvolution (AGRL)

Alberto De Luca1,2, Alexander Leemans2, Chantal MW Tax2,3, Kurt G Schilling4, and Geert-Jan Biessels1
1Neurology Department, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands, 2Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands, 3Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom, 4Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States


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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