Joint Maximum Likelihood Estimation of Motion and T-1 Parameters from Magnetic Resonance Images in a Super-resolution Framework: a Simulation Study

Magnetic resonance imaging (MRI) based T-1 mapping allows spatially resolved quantification of the tissue-dependent spin-lattice relaxation time constant T-1, which is a potential biomarker of various neurodegenerative diseases, including Multiple Sclerosis, Alzheimer disease, and Parkinson's disease. In conventional T-1 MR relaxometry, a quantitative T-1 map is obtained from a series of T-1-weighted MR images. Acquiring such a series, however, is time consuming. This has sparked the development of more efficient T-1 mapping methods, one of which is a super-resolution reconstruction (SRR) framework in which a set of low resolution (LR) T-1-weighted images is acquired and from which a high resolution (HR) T-1 map is directly estimated.

In this paper, the SRR T-1 mapping framework is augmented with motion estimation. That is, motion between the acquisition of the LR T-1-weighted images is modeled and the motion parameters are estimated simultaneously with the T-1 parameters. Based on Monte Carlo simulation experiments, we show that such an integrated motion/relaxometry estimation approach yields more accurate T-1 maps compared to a previously reported SRR based T-1 mapping approach.