Balbastre, Y;
Brudfors, M;
Azzarito, M;
Lambert, C;
Callaghan, MF;
Ashburner, J;
(2020)
Joint Total Variation ESTATICS for Robust Multi-parameter Mapping.
In: Martel, A and Abolmaesumi, P and Stoyanov, D and Mateus, D and Zuluaga, M and Zhou, SK and Racoceanu, D and Joskowicz, L, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020.
(pp. pp. 53-63).
Springer: Lima, Peru.
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Abstract
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific parameters – such as the apparent transverse relaxation rate R ?2, the longitudinal relaxation rate R 1 and the magnetisation transfer saturation – that can be compared across sites and scanners and carry important information about the underlying microstructure. The multiparameter 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 R ?2 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 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.
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