Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, Super Resolution, Partial Fourier, Reconstruction
Motivation: Current approaches using neural networks for combined super-resolution and partial Fourier (PF) reconstruction require separate models for each PF factor and direction resulting in increased training and maintenance efforts.
Goal(s): Develop a single deep learning model capable of handling any PF range and direction, eliminating the need for multiple networks.
Approach: Input images for training are zero-padded in the Fourier domain to simulate randomly varying PF from 75% to 100% in all three acquisition directions.
Results: Compared to zero-filling and models trained for specific PF factors, the proposed approach shows improved sharpness, reduced ringing artifacts, and enhanced quantitative metrics.
Impact: Our unified network performs super-resolution and PF reconstruction across a large range of PF factors applied in arbitrary directions. This removes the need for multiple dedicated networks trained for specific PF factors and simplifies pre- and post-processing operations.
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