A Unified Maximum Likelihood Framework for Simultaneous Motion and T-1 Estimation in Quantitative MR T-1 Mapping

作者：Ramos Llorden Gabriel; den Dekker Arnold J; Van Steenkiste Gwendolyn; Jeurissen Ben; Vanhevel Floris; Van Audekerke Johan; Verhoye Marleen; Sijbers Jan

来源：IEEE Transactions on Medical Imaging, 2017, 36(2): 433-446.

DOI：10.1109/TMI.2016.2611653

• Dynamic MRI
• maximum likelihood
• motion correction
• registration
• T-1 mapping

In quantitative MR T-1 mapping, the spin-lattice relaxation time T-1 of tissues is estimated from a series of T-1-weighted images. As the T-1 estimation is a voxel-wise estimation procedure, correct spatial alignment of the T-1-weighted images is crucial. Conventionally, the T-1-weighted images are first registered based on a general-purpose registration metric, after which the T-1 map is estimated. However, as demonstrated in this paper, such a two-step approach leads to a bias in the final T-1 map. In our work, instead of considering motion correction as a preprocessing step, we recover the motion-free T-1 map using a unified estimation approach. In particular, we propose a unified framework where the motion parameters and the T-1 map are simultaneously estimated with a Maximum Likelihood (ML) estimator. With our framework, the relaxation model, the motion model as well as the data statistics are jointly incorporated to provide substantially more accurate motion and T-1 parameter estimates. Experiments with realistic Monte Carlo simulations show that the proposed unified ML framework outperforms the conventional two-step approach as well as state-of-the-art model-based approaches, in terms of both motion and T-1 map accuracy and mean-square error. Furthermore, the proposed method was additionally validated in a controlled experiment with real T-1-weighted data and with two in vivo human brain T-1-weighted data sets, showing its applicability in real-life scenarios.

• 出版日期2017-2