Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDU

1 code implementation • 4 Oct 2022 • Charles Millard, Mark Chiew

Robust SSDU trains the reconstruction network to map from a further noisy and sub-sampled version of the data to the original, singly noisy and sub-sampled data, and applies an additive Noisier2Noise correction term at inference.

Denoising MRI Reconstruction +1

A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noise

1 code implementation • 20 May 2022 • Charles Millard, Mark Chiew

The development and understanding of self-supervised methods, which use only sub-sampled data for training, are therefore highly desirable.

Denoising Image Restoration +2

Tuning-free multi-coil compressed sensing MRI with Parallel Variable Density Approximate Message Passing (P-VDAMP)

1 code implementation • 8 Mar 2022 • Charles Millard, Mark Chiew, Jared Tanner, Aaron T. Hess, Boris Mailhe

To our knowledge, P-VDAMP is the first algorithm for multi-coil MRI data that obeys a state evolution with accurately tracked parameters.