https://scholars.lib.ntu.edu.tw/handle/123456789/611663
標題: | Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss | 作者: | Ouyang J. Chen K.T. Gong E. Pauly J. Zaharchuk G. TZE-HSIANG CHEN |
關鍵字: | Deep learning;Glycoproteins;Image quality;Mean square error;Positron emission tomography;Quality control;Signal to noise ratio;Adversarial learning;Adversarial networks;Peak signal to noise ratio;PET reconstruction;Positron emission tomography (PET);Root mean square errors;Structural similarity;Visual qualities;Magnetic resonance imaging;Article;brain cortex;classifier;clinical article;clinical evaluation;clinical feature;diagnostic accuracy;generative adversarial network;human;image quality;image reconstruction;machine learning;mean absolute error;nuclear magnetic resonance imaging;positron emission tomography;radiation dose;radiological parameters;signal noise ratio;statistical analysis;task positive network;time of flight mass spectrometry;article;deep learning;human experiment;low drug dose;radiologist | 公開日期: | 2019 | 卷: | 46 | 期: | 8 | 起(迄)頁: | 3555-3564 | 來源出版物: | Medical Physics | 摘要: | 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. ? 2019 American Association of Physicists in Medicine |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067498993&doi=10.1002%2fmp.13626&partnerID=40&md5=85ed0287896a5b52e184273e8f88750f https://scholars.lib.ntu.edu.tw/handle/123456789/611663 |
ISSN: | 00942405 | DOI: | 10.1002/mp.13626 |
顯示於: | 醫學工程學研究所 |
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