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Ultra‐low‐dose PET reconstruction using generative adversarial network with feature matching and task‐specific perceptual loss
Author(s) -
Ouyang Jiahong,
Chen Kevin T.,
Gong Enhao,
Pauly John,
Zaharchuk Greg
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13626
Subject(s) - artificial intelligence , discriminator , computer science , image quality , feature (linguistics) , pattern recognition (psychology) , positron emission tomography , iterative reconstruction , mean squared error , computer vision , nuclear medicine , mathematics , medicine , image (mathematics) , statistics , detector , telecommunications , linguistics , philosophy
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 them. The two networks contested with each other to achieve high‐visual quality PET from the ultra‐low‐dose PET. Multi‐slice inputs were used to reduce noise by providing the network with 2.5D information. Feature matching was applied to reduce hallucinated structures. Task‐specific perceptual loss was designed to maintain the correct pathological features. The image quality was evaluated by peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) metrics with and without each of these modules. Two expert radiologists were asked to score image quality on a 5‐point scale and identified the amyloid status (positive or negative). Results With only low‐dose PET as input, the proposed method significantly outperformed Chen et al.'s method (Chen et al. Radiology . 2018;290:649–656) (which shows the best performance in this task) with the same input (PET‐only model) by 1.87 dB in PSNR, 2.04% in SSIM, and 24.75% in RMSE. It also achieved comparable results to Chen et al.'s method which used additional magnetic resonance imaging (MRI) inputs (PET‐MR model). Experts' reading results showed that the proposed method could achieve better overall image quality and maintain better pathological features indicating amyloid status than both PET‐only and PET‐MR models proposed by Chen et al. Conclusion Standard‐dose amyloid PET images can be synthesized from ultra‐low‐dose images using GAN. Applying adversarial learning, feature matching, and task‐specific perceptual loss are essential to ensure image quality and the preservation of pathological features.