Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction

Abstract

Purpose: To introduce a combined machine learning (ML)- and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high-vresolution structural and diffusion imaging. Methods: Single-shot EPI is an efficient encoding technique, but does not lend itself well to high-resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot-to-shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC-SENSE) reconstruction to utilize data from all shots and improve upon the ML solution. Results: Our combined ML + physics approach enabled R-inplane x multiband (MB) = 8-x 2-fold acceleration using 2 EPI shots for multiecho imaging, so that whole-brain T-2 and T-2* parameter maps could be derived from an 8.3-second acquisition at 1 x 1 x 3-mm(3) resolution. This has also allowed high-resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane x MB = 9- x 2-fold acceleration. To make these possible, we extended the state-of-the-art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network. Conclusion: Combination of ML and JVC-SENSE enabled navigator-free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end-to-end ML approaches.