Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks

Volume: 64, Issue: 21, Pages: 215017 - 215017
Published: Nov 4, 2019
Abstract
Lowering either the administered activity or scan time is desirable in PET imaging as it decreases the patient's radiation burden or improves patient comfort and reduces motion artifacts. But reducing these parameters lowers overall photon counts and increases noise, adversely impacting image contrast and quantification. To address this low count statistics problem, we propose a cycle-consistent generative adversarial network (Cycle GAN) model...
Paper Details
Title
Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks
Published Date
Nov 4, 2019
Volume
64
Issue
21
Pages
215017 - 215017
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