Paired phase and magnitude reconstruction neural network for multi-shot diffusion magnetic resonance imaging

Abstract

Diffusion weighted imaging (DWI) is an important magnetic resonance imaging modality that reflects the diffusion of water molecules and has been widely used in tumor diagnosis. Higher image resolution is possible through multi-shot sampling but raises the challenge of suppressing image artifacts and noise when combining multi-shot data. Conventional methods introduce the magnitude and/or phase priors and regularize the reconstructed image in an iterative computing process, which suffers from slow computational speed. Deep learning offers a valuable solution to this challenge. In this work, traditional methods are adopted to generate the training labels offline. Then, a neural network is designed for paired phase and magnitude reconstruction. Last, the network is further improved by incorporating a high signal-to-noise ratio (SNR) b0 image with small geometric distortions. Compared with the state-of-the-art deep learning approach, results on simulated and in vivo data demonstrate that the proposed method enables sub-second fast reconstruction and achieves better objective evaluation criteria. Besides, a study by six radiologists on image quality confirms that the proposed method is within the excellent range and provides higher scores of image artifact suppression and more stable overall quality as well as SNR. This work provides a solution for fast and promising image reconstruction for multi-shot DWI.