Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss.

ABSTRACT:

Purpose

Our goal was to use a generative adversarial network (GAN) with feature matching and task-specific perceptual loss to synthesize standard-dose amyloid Positron emission tomography (PET) images of high quality and including accurate pathological features from ultra-low-dose PET images only.

Methods

Forty PET datasets from 39 participants were acquired with a simultaneous PET/MRI scanner following injection of 330 ± 30 MBq of the amyloid radiotracer 18F-florbetaben. The raw list-mode PET data were reconstructed as the standard-dose ground truth and were randomly undersampled by a factor of 100 to reconstruct 1% low-dose PET scans. A 2D encoder-decoder network was implemented as the generator to synthesize a standard-dose image and a discriminator was used to evaluate t

SUBMITTER: Ouyang J 

PROVIDER: S-EPMC6692211 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature