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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We have demonstrated that the GRASS acquisition enables flexible reconstruction of the same raw data as both time-resolved and cardiac-gated volumes. This may enable better interrogation of anatomy in congenital heart disease.

Background Computational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, typically obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. However, segmentation is time-consuming and requires expert input. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries for CFD studies. Methods 90 CHD patients were retrospectively selected for this study. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and pulmonary artery labels. These were used to train and optimize a U-Net model, using a 70-10-10 train-validation-test split. Segmentation performance was primarily evaluated using Dice score. CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Mean pressure and velocity fields across 99 planes along the vessel centrelines were extracted, and a mean average percentage error (MAPE) was calculated for each vessel pair (ML vs GT). A second observer (SO) segmented the test dataset for assessment of inter-observer variability. Friedman tests were used to compare ML vs GT, SO vs GT and ML vs SO metrics, and pressure/velocity field errors. Results The network’s Dice score (ML vs GT) was 0.945 (interquartile range: 0.929–0.955) for the aorta and 0.885 (0.851–0.899) for the pulmonary arteries. Differences with the inter-observer Dice score (SO vs GT) and ML vs SO Dice scores were not statistically significant for either aorta or pulmonary arteries (p = 0.741, p = 0.061). The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% (8.5–15.7%) and 4.1% (3.1–6.9%), respectively, and for the pulmonary arteries 14.6% (11.5–23.2%) and 6.3% (4.3–7.9%), respectively. Inter-observer (SO vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to ML vs GT (p > 0.2). Conclusions ML can successfully segment the great vessels for CFD, with errors similar to inter-observer variability. This fast, automatic method reduces the time and effort needed for CFD analysis, making it more attractive for routine clinical use.

We present TensorFlow MRI, a new open-source library of TensorFlow operators for MR image reconstruction and processing. Its goal is to enable fast prototyping of modern MRI applications within a single computing framework. It is intended for researchers working in MR image reconstruction and/or processing, especially those interested in ML applications. The library is primarily Python-based and is easy to use, understand and extend. It has a single-command installation procedure and extensive documentation. Thanks to the use of a TensorFlow backend, it has excellent performance and runs on heterogenous and distributed systems.