Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.

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 data by enforcing consistency between model reconstructions of undersampled scans and their noise-augmented counterparts.

Musical mirror-symmetrical movement tasks: comparison of rhythm versus melody-playing.

Bimanual mirror-symmetrical movement (MSM) is relatively easy to control movement. Different MSM tasks may have different activations and interhemispheric interactions. The purpose of this study is to compare anatomo-physiological features such as hemispheric activations and dominance of two different MSMs, namely melody-playing and rhythm.