Convolutional micro-networks for MR-guided low dose PET post-processing

Casper O. da Costa-Luis, Andrew J. Reader

Abstract: We propose a novel micro-network (mu-net) architecture which is a convolutional neural network (CNN) designed to be robust to minimal training data. Applying the mu-net to MR-guided low count PET scans produces impressive qualitative and quantitative cross-validation results despite training on just two noise realisations of the same patient.

Author Affiliation: King's College London

Keywords: CNN, image processing, guided reconstruction, PET-MR