Populational and individual information based PET image denoising using conditional unsupervised learning
Volume: 66, Issue: 15, Pages: 155001 - 155001
Published: Jul 19, 2021
Abstract
Our study aims to improve the signal-to-noise ratio of positron emission tomography (PET) imaging using conditional unsupervised learning. The proposed method does not require low- and high-quality pairs for network training which can be easily applied to existing PET/computed tomography (CT) and PET/magnetic resonance (MR) datasets. This method consists of two steps: populational training and individual fine-tuning. As for populational...
Paper Details
Title
Populational and individual information based PET image denoising using conditional unsupervised learning
Published Date
Jul 19, 2021
Volume
66
Issue
15
Pages
155001 - 155001
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