Keywords: Image Reconstruction, AI/ML Image Reconstruction
Motivation: Low-rank methods can reconstruct multi-dimensional NMR spectra with high quality but need huge time.
Goal(s): Develop a robust and high efficient deep learning method based on the low-rank prior.
Approach: We utilize the rank-one property of the exponential function in each dimension of NMR spectra and propose Rank-One Approximated Decomposition (ROAD) network. ROAD consists of four modules for peak retrieval, fast rank-one approximation, data consistency and factor matrix update module.
Results: Compared to other methods, experiments on synthetic 2D signals and realistic 3D/4D NMR signals show that ROAD can reconstruct signals with less error and preserve low-intensity peaks more reliably.
Impact: Designed by fast low-rank approximation, neural network correction and peak retrieval, ROAD shows the robust reconstruction benefited from optimization and fast computation from deep learning. Instead of reconstructing high-dimensional signals directly, ROAD reconstructs signals in each dimension with high efficiency.
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