Lightweight encoder-decoder architecture for Fat-Water separation in MRI using biophysical model-guided deep learning method

In this study, we propose a novel lightweight encoder-decoder architecture for deep learning based Fat-Water separation in multi-echo MRI data. The architecture's performance is evaluated in the biophysical model-guided deep learning-based Fat-Water separation task and compared against the widely used U-Net. This biophysical model-guided deep learning-based Fat-Water separation requires no training data and ground truths, but it involves time-consuming loss minimization for thousands of epochs. Despite having significantly fewer training parameters, the proposed architecture performed equally well in generating the Fat-Water maps compared to the U-Net. So, our proposed architecture aids in the faster generation of Fat-Water maps.