Water–fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network

Background Water–fat separation is a postprocessing technique most commonly applied to multiple‐gradient‐echo magnetic resonance (MR) images to identify fat, provide images with fat suppression, and to measure fat tissue concentration. Recently, Numerous advancements have been reported. In contrast to early methods, the process of water–fat separation has become complicated due to multiparametric analytic models, optimization methods, and the absence of a unified framework for diverse source data. Purpose To determine the feasibility and performance of MRI water–fat separation and parametric mapping via deep learning (DL) with a range of inputs. Study Type Retrospective data usage. Population/Subjects Ninety cardiac MR examinations from normal control, acute, subacute, and chronic myocardial infarction subjects were obtained, providing 1200 multiple gradient‐echo acquisitions. Field Strength/Sequence 1.5 T/2D multiple gradient‐echo pulse sequence Assessment Ground‐truth training and validation water–fat separation were obtained using a graph cut method with R 2 *, off‐resonance correction, and a multipeak fat spectrum. U‐Net DL training with single and multiecho, complex, and magnitude inputs were compared using quantitative and three‐observer subjective analysis. Statistical Tests DL methods' image structural similarity, and quantitative proton density fat fraction (PDFF), R 2 *, and off‐resonance quantitative values were statistically compared with the GraphCut reference standard using Student's t ‐test and Pearson's correlation. Results Myocardial fat deposition in chronic myocardial infarction and intramyocardial hemorrhage in acute myocardial infarction were well visualized in the DL results. Predicted values for R 2 *, off‐resonance, water, and fat signal intensities were well correlated with a conventional model‐based water fat separation (R 2 ≥ 0.97, P < 0.001) with appropriate inputs. DL parameter maps had a 14% higher signal‐to‐noise ratio ( P < 0.001) when compared with a conventional method. Data Conclusion DL water–fat separation is feasible with a wide range of inputs, while R 2 * and off‐resonance mapping requires multiple echoes and complex images. With appropriate inputs, DL provides quantitative and subjective results comparable to conventional model‐based methods. Level of Evidence : 1 Technical Efficacy Stage : 1 J. Magn. Reson. Imaging 2019;50:655–665.