Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction.A wide range of approaches have been proposed, which can be applied in k-space and/or image-space.Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation.In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends.

Background Contrast‐enhanced magnetic resonance angiography (MRA) is used to assess various cardiovascular conditions. However, gadolinium‐based contrast agents (GBCAs) carry a risk of dose‐related adverse effects. Purpose To develop a deep learning method to reduce GBCA dose by 80%. Study Type Retrospective and prospective. Population A total of 1157 retrospective and 40 prospective congenital heart disease patients for training/validation and testing, respectively. Field Strength/Sequence A 1.5 T, T1‐weighted three‐dimensional (3D) gradient echo. Assessment A neural network was trained to enhance low‐dose (LD) 3D MRA using retrospective synthetic data and tested with prospective LD data. Image quality for LD (LD‐MRA), enhanced LD (ELD‐MRA), and high‐dose (HD‐MRA) was assessed in terms of signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), and a quantitative measure of edge sharpness and scored for perceptual sharpness and contrast on a 1–5 scale. Diagnostic confidence was assessed on a 1–3 scale. LD‐ and ELD‐MRA were assessed against HD‐MRA for sensitivity/specificity and agreement of vessel diameter measurements (aorta and pulmonary arteries). Statistical Tests SNR, CNR, edge sharpness, and vessel diameters were compared between LD‐, ELD‐, and HD‐MRA using one‐way repeated measures analysis of variance with post‐hoc t‐tests. Perceptual quality and diagnostic confidence were compared using Friedman's test with post‐hoc Wilcoxon signed‐rank tests. Sensitivity/specificity was compared using McNemar's test. Agreement of vessel diameters was assessed using Bland–Altman analysis. Results SNR, CNR, edge sharpness, perceptual sharpness, and perceptual contrast were lower (P < 0.05) for LD‐MRA compared to ELD‐MRA and HD‐MRA. SNR, CNR, edge sharpness, and perceptual contrast were comparable between ELD and HD‐MRA, but perceptual sharpness was significantly lower. Sensitivity/specificity was 0.824/0.921 for LD‐MRA and 0.882/0.960 for ELD‐MRA. Diagnostic confidence was 2.72, 2.85, and 2.92 for LD, ELD, and HD‐MRA, respectively (PLD‐ELD, PLD‐HD < 0.05). Vessel diameter measurements were comparable, with biases of 0.238 (LD‐MRA) and 0.278 mm (ELD‐MRA). Data Conclusion Deep learning can improve contrast in LD cardiovascular MRA. Level of Evidence Level 2 Technical Efficacy Stage 2

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