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Found 3 papers, 2 papers with code
Rician likelihood loss for quantitative MRI using self-supervised deep learning
Results: Networks trained with NLR loss show higher estimation accuracy than MSE for the ADC and IVIM diffusion coefficients as SNR decreases, with minimal loss of precision or total error.
Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning
In this paper, we demonstrate self-supervised machine learning model fitting for a directional microstructural model.
Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation
Self-supervised approaches, sometimes referred to as unsupervised, have been loosely based on auto-encoders, whereas supervised methods have, to date, been trained on groundtruth labels.
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