NDLI: DRGAN: a deep residual generative adversarial network for PET image reconstruction

Content Provider	IET Digital Library
Author	Du, Qianqian Qiang, Yan Yang, Wenkai Wang, Yanfei Ma, Yong Zia, Muhammad Bilal
Abstract	Positron emission tomography (PET) image reconstruction from low-count projection data and physical effects is challenging because the inverse problem is ill-posed and the resultant image is usually noisy. Recently, generative adversarial networks (GANs) have also shown their superior performance in many computer vision tasks and attracted growing interests in medical imaging. In this work, the authors proposed a novel model [deep residual generative adversarial network (DRGAN)] based on GANs for the reduction of streaking artefacts and the improvement of PET image quality. An innovative feature of the proposed method is that the authors trained a generator to produce ‘residual PET map’ (RPM) for image representation, rather than generate PET images directly. DRGAN used two discriminators (critics) to enforce anatomically realistic PET images and RPM. To better boost the contextual information, the authors designed residual dense connections followed with pixel shuffle operations (RDPS blocks) that encourage feature reuse and prevent losing resolution. Both simulation data and real clinical PET data are used to evaluate the proposed method. Compared with other state-of-the-art methods, the quantification results show that DRGAN can achieve better performance in bias–variance trade-off and provide comparable image quality. Their results were rigorously evaluated by one radiologist at the Shanxi Cancer Hospital.
Starting Page	1690
Ending Page	1700
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 9, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1107
Journal	IET Image Processing
Publisher Date	2020-01-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Anatomically Realistic PET Image Biology And Medical Computing Clinical PET Data Computer Vision Computer Vision And Image Processing Technique Computer Vision Tasks Deep Residual Generative Adversarial Network DRGAN Image Reconstruction Image Representation Image Resolution Inverse Problem Low-count Projection Data Medical Image Medical Image Processing Neural Computing Technique Neural Nets Nuclear Medicine, Emission Tomography Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation PET Image Quality PET Image Reconstruction Physical Effect Pixel Shuffle Operation Positron Emission Tomography Positron Emission Tomography Image Reconstruction Residual Dense Connections Residual PET Map RPM Simulation Data Streaking Artefact Reduction
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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