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A coupled-mechanisms modelling framework for neurodegeneration
We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects.
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.
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