Abstract #4339

T1 Mapping through Bayesian Analysis with Spatial Information Collaboration (BASIC) using Steady-State-Based Imaging Data

Mustapha Bouhrara¹ and Richard G. Spencer¹

¹NIA, NIH, Baltimore, MD, United States

We introduce two Bayesian-based analyses that use spatial information as a prior to improve the quality of voxel-by-voxel T₁-mapping from spoiled gradient recalled echo (SPGR) imaging data. These approaches, called BASIC, combine voxel-by-voxel fitting with region-of-interest (ROI) parameter estimation. ROI parameters act as a constraint, while voxel fitting mitigates blurring and detail loss. The results were compared with those derived using a conventional nonlinear least-squares-based algorithm. Estimation of T₁ from SPGR imaging data was markedly improved through use of the BASIC methods.

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