We thank Marlies Hankel and Jake Carroll for enabling the access to the necessary compute capabilities, Joshua Arnold for initial discussions and implementations that laid the foundations of this project and Matthew Cronin for valuable feedback on the bioRxiv preprint.

Summary Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of deep learning in MR image reconstruction from different image acquisition types involving compressed sensing techniques, parallel image acquisition and multi‐contrast imaging. Publications with deep learning‐based image reconstruction for MR imaging were identified from the literature (PubMed and Google Scholar), and a comprehensive description of each of the works was provided. A detailed comparison that highlights the differences, the data used and the performance of each of these works were also made. A discussion of the potential use cases for each of these methods is provided. The sparse image reconstruction methods were found to be most popular in using deep learning for improved performance, accelerating acquisitions by around 4–8 times. Multi‐contrast image reconstruction methods rely on at least one pre‐acquired image, but can achieve 16‐fold, and even up to 32‐ to 50‐fold acceleration depending on the set‐up. Parallel imaging provides frameworks to be integrated in many of these methods for additional speed‐up potential. The successful use of compressed sensing techniques and multi‐contrast imaging with deep learning and parallel acquisition methods could yield significant MR acquisition speed‐ups within clinical routines in the near future.

Purpose: Quantitative susceptibility mapping (QSM) estimates the spatial distribution of tissue magnetic susceptibilities from the phase of a gradient-echo signal. QSM algorithms require a signal mask to delineate regions with reliable phase for subsequent susceptibility estimation. Existing masking techniques used in QSM have limitations that introduce artifacts, exclude anatomical detail, and rely on parameter tuning and anatomical priors that narrow their application. Here, a robust masking and reconstruction procedure is presented to overcome these limitations and enable automated QSM processing. Moreover, this method is integrated within an open-source software framework: QSMxT. Methods:A robust masking technique that automatically separates reliable from less reliable phase regions was developed and combined with a two-pass reconstruction procedure that operates on the separated sources before combination, extracting more information and suppressing streaking artifacts.