A Unified Maximum Likelihood Framework for Simultaneous Motion and $T_{1}$ Estimation in Quantitative MR $T_{1}$ Mapping.
In quantitative MR T1mapping, the spin-lattice relaxation time T1of tissues is estimated from a series of T1-weighted images. As the T1estimation is a voxel-wise estimation procedure, correct spatial alignment of the T1-weighted images is crucial. Conventionally, the T1-weighted images are first registered based on a general-purpose registration metric, after which the T1map is estimated. However, as demonstrated in this paper, such a two-step approach leads to a bias in the final T1map. In our work, instead of considering motion correction as a preprocessing step, we recover the motion-free T1map using a unified estimation approach. In particular, we propose a unified framework where the motion parameters and the T1map 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 T1parameter 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 T1map accuracy and mean-square error. Furthermore, the proposed method was additionally validated in a controlled experiment with real T1-weighted data and with two in vivo human brain T1-weighted data sets, showing its applicability in real-life scenarios.
Gabriel
G
Ramos-Llorden
Arnold J
AJ
den Dekker
Gwendolyn
G
Van Steenkiste
Ben
B
Jeurissen
Floris
F
Vanhevel
Johan
J
Van Audekerke
Marleen
M
Verhoye
Jan
J
Sijbers
D000465:Algorithms; D006801:Humans; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging; D009010:Monte Carlo Method; D009038:Motion; D015203:Reproducibility of Results