Purpose To accelerate chemical shift encoded (CSE) water–fat imaging by applying a model‐guided deep learning water–fat separation (MGDL‐WF) framework to the undersampled k‐space data. Methods A model‐guided deep learning water–fat separation framework is proposed for the acceleration using Cartesian/radial undersampling data. The proposed MGDL‐WF combines the power of CSE water–fat imaging model and data‐driven deep learning by jointly using a multi‐peak fat model and a modified residual U‐net network. The model is used to guide the image reconstruction, and the network is used to capture the artifacts induced by the undersampling. A data consistency layer is used in MGDL‐WF to ensure the output images to be consistent with the k‐space measurements. A Gauss‐Newton iteration algorithm is adapted for the gradient updating of the networks. Results Compared with the compressed sensing water–fat separation (CS‐WF) algorithm/2‐step procedure algorithm, the MGDL‐WF increased peak signal‐to‐noise ratio (PSNR) by 5.31/5.23, 6.11/4.54, and 4.75 dB/1.88 dB with Cartesian sampling, and by 4.13/6.53, 2.90/4.68, and 1.68 dB/3.48 dB with radial sampling, at acceleration rates (R) of 4, 6, and 8, respectively. By using MGDL‐WF, radial sampling increased the PSNR by 2.07 dB at R = 8, compared with Cartesian sampling. Conclusions The proposed MGDL‐WF enables exploiting features of the water images and fat images from the undersampled multi‐echo data, leading to improved performance in the accelerated CSE water–fat imaging. By using MGDL‐WF, radial sampling can further improve the image quality with comparable scan time in comparison with Cartesian sampling.

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