Technical note: Revised projections onto convex sets reconstruction of multi‐shot diffusion‐weighted imaging

摘要

Abstract Background High‐resolution diffusion‐weighted imaging (DWI) is usually achieved through multi‐shot acquisitions and parallel imaging‐based reconstructions. Multiple POCS (projections onto convex sets) based algorithms have been proposed for DWI reconstructions. However, the slow convergence of POCS and the suboptimal quality of the reconstructed images limit their applications. Purpose In this study, a revised POCS algorithm for multi‐shot DWI reconstruction is proposed based on FISTA (fast iterative shrinkage‐thresholding algorithm) to achieve faster convergence and higher accuracy. Methods In FISTA, the next iteration is computed based on two previous iterations, instead of only the previous one, to improve the convergence speed. This scheme is adopted into the relevant POCS‐based algorithms, including POCSENSE (POCS‐based sensitivity‐encoding), POCSMUSE (POCS‐based multiplexed sensitivity‐encoding), iPOCSMUSE (iterative POCSMUSE), and POCS‐ICE (POCS‐enhanced inherent correction of motion‐induced phase errors) to address the slow convergence problem. Simulations and in vivo experiments were performed to evaluate the performance of the proposed method. Results Experimental results show that the proposed method enables faster convergence compared to the original POCS. For example, for a spiral DWI simulation using eight‐shot interleaves and having SNR of 20?dB, the iteration number needed for the revised POCS‐ICE decreases by about 70 to achieve approximately the same nRMSE level as POCS‐ICE. Additionally, it improves image quality in terms of fewer artifacts compared with the original POCS. Conclusions The revised DWI reconstruction methods can achieve higher convergence rates than the original POCS‐based algorithms and higher image quality with the same iteration numbers. As such, the proposed method can serve as a practical and efficient reconstruction method for multi‐shot DWI.