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Purpose
To develop a method for building MRI reconstruction neural networks robust to changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled scans.
Methods
We propose Noise2Recon, a consistency training method for SNR‐robust accelerated MRI reconstruction that can use both fully sampled (labeled) and undersampled (unlabeled) scans. Noise2Recon uses unlabeled...
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