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eess.IV2022
Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDU
Charles Millard, Mark Chiew
Most existing methods for Magnetic Resonance Imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a high signal-to-noise ratio (SNR), f…
eess.IV2022★ 1 cited
A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise
Charles Millard, Mark Chiew
In recent years, there has been attention on leveraging the statistical modeling capabilities of neural networks for reconstructing sub-sampled Magnetic Resonance Imaging (MRI) dat…