Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise.

Purpose: To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. Methods: List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and...

Full description

Bibliographic Details

Published in:	European Journal of Nuclear Medicine & Molecular Imaging Vol. 49; no. 2; pp. 539 - 550
Main Authors:	Mehranian, Abolfazl, Wollenweber, Scott D., Walker, Matthew D., Bradley, Kevin M., Fielding, Patrick A., Su, Kuan-Hao, Johnsen, Robert, Kotasidis, Fotis, Jansen, Floris P., McGowan, Daniel R.
Format:	diagnostic images research tables/charts Journal Article
Published:	Springer Nature Jan2022
Online Access:	View this record in EBSCOhost