2 papers
eess.IV2026
Optimized Multi-Contrast Self-Supervised MRI Reconstruction using Learned k-space Partitioning
Brenden Kadota, Charles Millard, Mark Chiew
Objective: Deep Learning has shown promise in accelerating MRI by reconstructing high-quality images from under-sampled data. While recent work has leveraged multi-contrast informa…
eess.IV2024
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…