POSE: POSition Encoding for accelerated MRI

Motivation: Quantitative MRI requires multiple acquisitions to sufficiently characterize a biophysical-model of interest, resulting in long scan times. Goal(s): Propose a new general strategy for accelerating MRI using subvoxel-shifting as a means of encoding. Approach: POSition Encoding (POSE) applies unique subvoxel-shifts along the acquisition parameter dimension, which is combined with a biophysical-model to generate accelerated and enhanced resolution biophysical-parameter maps. Using T1 quantification as an application, POSE was validated both numerically and experimentally. Results: Results show that POSE 1) generates quantitative maps that agree well with the reference method, 2) has robust noise performance and 3) is applicable to any biophysical-model of interest. Impact: POSE combines subvoxel-scale spatial encoding with biophysical-models to achieve enhanced resolution with acceleration. Its ease of implementation, robust noise performance and versatility makes it an attractive framework applicable to any quantitative biophysical-model of interest.