In this paper we introduced a motion-compensated model estimation technique for renal DW-MRI. The technique has two main components: 1) we adapted an approach based on robust state estimation, which was recently utilized to solve slice-based motion estimation, to track physiological motion (including respiratory motion); 2) we used weighted least squares to estimate diffusion tensor model and calculate diffusion parameters from motion-compensated data. Overall, our method achieved the highest FA values in the medulla, compared to no motion correction and volume to volume registration which resulted in reduced FA values, artifacts, and blurrier FA, MD and AD maps.
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