Clean self-supervised MRI reconstruction from noisy, sub-sampled
training data with Robust SSDU
Abstract
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), fully sampled dataset
is available for training. In many circumstances, however, such a
dataset is highly impractical or even technically infeasible to acquire.
Recently, a number of self-supervised methods for MR reconstruction have
been proposed, which use sub-sampled data only. However, the majority of
such methods, such as Self-Supervised Learning via Data Undersampling
(SSDU), are susceptible to reconstruction errors arising from noise in
the measured data. In response, we propose Robust SSDU, which provably
recovers clean images from noisy, sub-sampled training data. 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 a Noisier2Noise correction term at
inference. We also present a related method, Noiser2Full, that recovers
clean images when noisy, fully sampled data is available for training.
Both proposed methods are applicable to any network architecture,
straight-forward to implement and have similar computational cost to
standard training. We evaluate our methods on the multi-coil fastMRI
brain dataset with a novel denoising-specific architecture and find that
it performs competitively with a benchmark trained on clean, fully
sampled data.