Simultaneous Off-Resonance Correction and Fat-Water Separation From Center-Out Spiral Acquisition Using a Physics-Informed DL Framework

Motivation: Accelerated MRI protocols and fat/water separation are critical in clinical imaging but are compromised by off-resonance artifacts from B0 inhomogeneities, particularly in non-Cartesian trajectories with longer readouts. Goal(s): We aim to develop a deep learning framework that enables off-resonance correction from Center-Out Spiral acquisitions, enhancing scan efficiency and image fidelity without extended acquisition times, with the added value of performing fat/water separation. Approach: Our physics-informed framework employs a multi-frequency bin model trained on synthetic noise data, enabling off-resonance deblurring and extraction of fat and water components without additional acquisition steps. Results: We showcase our model's efficacy through phantom and in-vivo reconstructions. Impact: Our physics-informed deep learning framework offers off-resonance correction in Non-Cartesian Spiral MRI, enabling rapid imaging. Our model handles partial volume effects, with the added value of providing fat/water image separation.