A method is disclosed for generating pixel risk maps for diagnostic image reconstruction. The method produces uncertainty/variance maps by feeding into a trained encoder a short-scan image acquired from a medical imaging scan to generate latent code statistics including the mean μy and variance σy; selecting random samples based on the latent code statistics, z˜N(μy,σy2); feeding the random samples into a trained decoder to generate a pool of reconstructed images; and calculating, for each pixel of the pool of reconstructed images, the pixel mean and variance statistics across the pool of reconstructed images. The risk of each pixel may be calculated using the Stein's Unbiased Risk Estimator on the input density compensated data, that involves calculating the end-to-end divergence of the deep neural network.
Methods for Risk Map Prediction in AI-based MRI Reconstruction