  Purpose: A new method for magnetic resonance (MR) imaging water-fat
separation using a convolutional neural network (ConvNet) and deep learning
(DL) is presented. Feasibility of the method with complex and magnitude images
is demonstrated with a series of patient studies and accuracy of predicted
quantitative values is analyzed.
  Methods: Water-fat separation of 1200 gradient-echo acquisitions from 90
imaging sessions (normal, acute and chronic myocardial infarction) was
performed using a conventional model based method with modeling of R2* and
off-resonance and a multi-peak fat spectrum. A U-Net convolutional neural
network for calculation of water-only, fat-only, R2* and off-resonance images
was trained with 900 gradient-echo Multiple and single-echo complex and
magnitude input data algorithms were studied and compared to conventional
extended echo modeling.
  Results: The U-Net ConvNet was easily trained and provided water-fat
separation results visually comparable to conventional methods. Myocardial fat
deposition in chronic myocardial infarction and intramyocardial hemorrhage in
acute myocardial infarction were well visualized in the DL results. Predicted
values for R2*, off-resonance, water and fat signal intensities were well
correlated with conventional model based water fat separation (R2>=0.97,
p<0.001). DL images had a 14% higher signal-to-noise ratio (p<0.001) when
compared to the conventional method.
  Conclusion: Deep learning utilizing ConvNets is a feasible method for MR
water-fat separationimaging with complex, magnitude and single echo image data.
A trained U-Net can be efficiently used for MR water-fat separation, providing
results comparable to conventional model based methods.
