Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors.

Journal: Magnetic resonance in medicine

Published Date: Aug 15, 2023

Abstract

PURPOSE: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.

Authors

Max Hellström

Department of Radiation Sciences, Umeå University, Umeå, Sweden.
Tommy Löfstedt

Department of Radiation Sciences, Umeå University, Umeå, Sweden.
Anders Garpebring

Department of Radiation Sciences, Umeå University, Umeå, 901 87, Sweden.

Keywords

Bayes Theorem Image Processing, Computer-Assisted Neural Networks, Computer Signal-To-Noise Ratio Uncertainty

External Resources

View on PubMed Access via DOI PubMed (37582257)

Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors.

Abstract

Authors

Keywords

External Resources

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