Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
This work addresses the need for more reliable and consistent tissue parameter estimation in clinical MRI, representing an incremental improvement over existing methods.
The paper tackled the problem of improving robustness and reducing variance in multi-parameter mapping for quantitative MRI by extending the ESTATICS model with a joint total variation prior and nonlinear maximum a posteriori estimation, resulting in outperforming state-of-the-art methods and greatly reducing variance without bias in validation.
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific parameters -- such as the apparent transverse relaxation rate R2*, the longitudinal relaxation rate R1 and the magnetisation transfer saturation -- that can be compared across sites and scanners and carry important information about the underlying microstructure. The multi-parameter mapping (MPM) protocol takes advantage of multi-echo acquisitions with variable flip angles to extract these parameters in a clinically acceptable scan time. In this context, ESTATICS performs a joint loglinear fit of multiple echo series to extract R2* and multiple extrapolated intercepts, thereby improving robustness to motion and decreasing the variance of the estimators. In this paper, we extend this model in two ways: (1) by introducing a joint total variation (JTV) prior on the intercepts and decay, and (2) by deriving a nonlinear maximum \emph{a posteriori} estimate. We evaluated the proposed algorithm by predicting left-out echoes in a rich single-subject dataset. In this validation, we outperformed other state-of-the-art methods and additionally showed that the proposed approach greatly reduces the variance of the estimated maps, without introducing bias.