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Abstract #0111

Incoherent k-q Under-sampled Multi-shot EPI for Accelerated Multi-shell Diffusion MRI with Model-based Deep Learning Reconstruction

Merry Mani1, Vincent Magnotta2, and Mathews Jacob2
1Radiology, University of Iowa, Iowa City, IA, United States, 2University of Iowa, Iowa City, IA, United States

We propose a new acceleration and reconstruction method for under-sampled multi-shot multi-shell dMRI. The method makes use of incoherent under-sampling in the joint k-q domain to achieve high acceleration. We develop a new model-based reconstruction that jointly recovers missing q-space points, by utilizing a q-space manifold prior that is pre-learned using deep learning. The proposed method is shown to accurately recover the DWIs from 8-fold accelerated multi-shell data. The reconstruction error is shown to be less than 3%. The proposed method enables utilization of multi-shot EPI trajectories for diffusion microstructure and connectivity studies requiring multi-shell coverage, without prolonging scan time.

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