Efficient Parallel Reconstruction for High Resolution Multishot Spiral Diffusion Data With Low Rank Constraint

Purpose: To propose a novel reconstruction method using parallel imaging with low rank constraint to accelerate high resolution multishot spiral diffusion imaging. Theory and Methods: The undersampled high resolution diffusion data were reconstructed based on a low rank (LR) constraint using similarities between the data of different interleaves from a multishot spiral acquisition. The self-navigated phase compensation using the low resolution phase data in the center of k-space was applied to correct shot-to-shot phase variations induced by motion artifacts. The low rank reconstruction was combined with sensitivity encoding (SENSE) for further acceleration. The efficiency of the proposed joint reconstruction framework, dubbed LR-SENSE, was evaluated through error quantifications and compared with ` 1 regularized compressed sensing method and conventional iterative SENSE method using the same datasets. Results: It was shown that with a same acceleration factor, the proposed LR-SENSE method had the smallest normalized sum-of-squares errors among all the compared methods in all diffusion weighted images and DTI-derived index maps, when evaluated with different acceleration factors (R = 2, 3, 4) and for all the acquired diffusion directions. Conclusion: Robust high resolution diffusion weighted image can be efficiently reconstructed from highly undersampled multishot spiral data with the proposed LR-SENSE method. (C) 2016 International Society for Magnetic Resonance in Medicine