Keywords: Other AI/ML, Fat and Fat/Water Separation
Motivation: Robust water-fat separation is crucial for nasal lesion detection and tracking. Existing methods often suffer from limited contrast and issues with water-fat swaps.
Goal(s): We aim to accurately extract water/fat-only images from multi-contrast nasal MRI using two-echo FSE sequences.
Approach: We propose a hybrid model and data driven network to provide reliable water-fat separation from two-echo signals based on end-to-end learning. A 3D physical model is also used to guide the network to learn layer-wise features.
Results: The proposed framework outperforms the representative methods, with PSNR of 50.57 and SSIM of 0.9983. Visually, our method also provides more precise separation of nasal structures.
Impact: Our proposed hybrid separation framework combines multi-task networks with a 3D physical model to enhance the robustness of water/fat separation in multi-contrast nasopharyngeal MRI images, significantly expanding its clinical applicability.
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