Methods for risk map prediction in AI-based MRI reconstruction

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United States of America Patent

PATENT NO 11776679
APP PUB NO 20210287780A1
SERIAL NO

17196838

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Abstract

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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(μyy2); 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.

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Patent Owner(s)

  • THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Donoho, David Stanford, US 4 404
Korani, Morteza Mardani Santa Clara, US 3 29
Pauly, John M Stanford, US 77 1135
Vasanawala, Shreyas S Stanford, US 30 226

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