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Laplacian-Regularized MAP-MRI: Improving Axonal Caliber Estimation

Abstract : In diffusion MRI, the accurate description of the entire diffusion signal from sparse measurements is essential to enable the recovery of microstructural information of the white matter. The recent Mean Apparent Propagator (MAP)-MRI basis is especially well suited for this task, but the basis fitting becomes unreliable in the presence of noise. As a solution we propose a fast and robust analytic Laplacian regularization for MAP-MRI. Using both synthetic diffusion data and human data from the Human Connectome Project we show that (1) MAP-MRI has more accurate microstructure recovery compared to classical techniques, (2) regularized MAP-MRI has lower signal fitting errors compared to the unregularized approach and a positivity constraint on the EAP and (3) that our regularization improves axon radius recovery on human data.
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Contributor : Rutger Fick Connect in order to contact the contributor
Submitted on : Tuesday, April 7, 2015 - 3:21:07 PM
Last modification on : Saturday, June 25, 2022 - 11:15:58 PM
Long-term archiving on: : Tuesday, April 18, 2017 - 12:42:40 PM


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  • HAL Id : hal-01140021, version 1



Rutger H.J. Fick, Demian Wassermann, Gonzalo Sanguinetti, Rachid Deriche. Laplacian-Regularized MAP-MRI: Improving Axonal Caliber Estimation. International Symposium on BIOMEDICAL IMAGING: From Nano to Macro, Apr 2015, Brooklyn, New York City, United States. ⟨hal-01140021⟩



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