Robust Spectral Denoising for Water-Fat Separation in Magnetic Resonance Imaging
Fat quantification based on the multi-echo Dixon method is gaining importance in clinical practice as it can match the accuracy of spectroscopy but provides high spatial resolution. Accurate quantification protocols, though, are limited to low SNR and suffer from a high noise bias. As the clinically relevant water and fat components are estimated by fitting a non-linear signal model to the data, the uncertainty is further amplified. In this work, we first establish the low-rank property and its locality assumptions for water-fat MRI and, consequently, propose a model-consistent but adaptive spectral denoising. A robust noise estimation in combination with a risk-minimizing threshold adds to a fully-automatic method. We demonstrate its capabilities on abdominal fat quantification data from in-vivo experiments. The denoising reduces the fit error on average by 37% and the uncertainty of the fat fraction by 58% in comparison to the original data while being edge-preserving.
KeywordsRobust Denoising Water-Fat MRI Locally Low-Rank
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