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.