Estimating T1 from Multichannel Variable Flip Angle SPGR Sequences with Graph Cuts

Quantitative measurements of T1 values have been used to monitor pathology. Recently a maximum likelihood estimator was created to obtain the best estimate of T1 values in the presence of noise, with no a priori assumption about image structure, for a variable flip angle spoiled gradient-recalled echo sequence. While this improved estimations, individual voxel estimates exhibited high variance. It is hypothesized that adding a spatial prior promoting piecewise smoothness will improve estimations. Since standard continuous optimization techniques are inefficient and unstable for this generalized nonlinear least squares problem, an iterative graph cut strategy was developed for the regularized T1-estimation.

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