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ResoNet: Noise-Trained Physics-Informed MRI Off-Resonance Correction
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Alfredo De Goyeneche and Shreya Ramachandran and Ke Wang and Ekin Karasan and Joseph Cheng and Stella X. Yu and Michael Lustig
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Neural Information Processing Systems, New Orleans, Louisiana, 9-15 December 2023
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Paper
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Poster
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Code
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Abstract
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Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality that offers diagnostic information without harmful ionizing radiation. Unlike optical imaging, MRI sequentially samples the spatial Fourier domain (\textit{k}-space) of the image. Measurements are collected in multiple shots, or \textit{readouts}, and in each shot, data along a smooth trajectory is sampled. Conventional MRI data acquisition relies on sampling \textit{k}-space row-by-row in short intervals, which is slow and inefficient. More efficient, non-Cartesian sampling trajectories (e.g., Spirals) use longer data readout intervals, but are more susceptible to magnetic field inhomogeneities, leading to off-resonance artifacts. Spiral trajectories cause off-resonance blurring in the image, and the mathematics of this blurring resembles that of optical blurring, where magnetic field variation corresponds to depth and readout duration to aperture size. Off-resonance blurring is a system issue with a physics-based, accurate forward model. We present a physics-informed deep learning framework for off-resonance correction in MRI, which is trained exclusively on synthetic, noise-like data with representative marginal statistics. Our approach allows for fat/water separation and is compatible with parallel imaging acceleration. Through end-to-end training using synthetic randomized data (\emph{i.e.}, noise-like images, coil sensitivities, field maps), we train the network to reverse off-resonance effects across diverse anatomies and contrasts without retraining. We demonstrate the effectiveness of our approach through results on phantom and \emph{in-vivo} data. This work has the potential to facilitate the clinical adoption of non-Cartesian sampling trajectories, enabling efficient, rapid, and motion-robust MRI scans.
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Keywords
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off-resonance correction, MRI, learning from noise
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