Why the study?
Cardiac MRI water-fat separation has become complicated by multiparametric analytic models, optimization methods, and the lack of a unified framework for diverse source data.
Does deep learning via a convolutional neural network improve water-fat separation and parameter mapping in cardiac MRI compared to conventional model-based methods?
Comparison
U-Net deep learning water-fat separation vs graph cut reference standard
Design
Retrospective study
Key result
Deep learning-based water-fat separation correlated highly with conventional model-based methods (R2 ≥ 0.97, P<0.001) and provided a 14% higher signal-to-noise ratio (P<0.001).
Authors
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Feasibility of multiparametric water-fat separation shown in limited data; leaves open unified frameworks for diverse MR sources.
Observational (n=90)
Does deep learning via a convolutional neural network improve water-fat separation and parameter mapping in cardiac MRI compared to conventional model-based methods?
Effect estimate: R2 ≥ 0.97
p-value: p=<0.001
Deep learning-based water-fat separation in cardiac MRI is feasible and provides quantitative results comparable to conventional methods with a higher signal-to-noise ratio.
Goldfarb et al. (2019) conducted an observational in Myocardial infarction (n=90). Deep learning (U-Net) water-fat separation vs. Conventional model-based water-fat separation (graph cut method) was evaluated on Correlation of predicted values for R2*, off-resonance, water, and fat signal intensities (R2 ≥ 0.97, p=<0.001). Deep learning-based water-fat separation correlated highly with conventional model-based methods (R2 ≥ 0.97, P<0.001) and provided a 14% higher signal-to-noise ratio (P<0.001).
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