PET Synthesis via Self-supervised Adaptive Residual Estimation
  Generative Adversarial Network
Yuxin Xue Lei Bi Yige Peng Michael Fulham David Dagan Feng Jinman Kim
Positron emission tomography (PET) is a widely used, highly sensitive
molecular imaging in clinical diagnosis. There is interest in reducing the
radiation exposure from PET but also maintaining adequate image quality. Recent
methods using convolutional neural networks (CNNs) to generate synthesized
high-quality PET images from low-dose counterparts have been reported to be
state-of-the-art for low-to-high image recovery methods. However, these methods
are prone to exhibiting discrepancies in texture and structure between
synthesized and real images. Furthermore, the distribution shift between
low-dose PET and standard PET has not been fully investigated. To address these
issues, we developed a self-supervised adaptive residual estimation generative
adversarial network (SS-AEGAN). We introduce (1) An adaptive residual
estimation mapping mechanism, AE-Net, designed to dynamically rectify the
preliminary synthesized PET images by taking the residual map between the
low-dose PET and synthesized output as the input, and (2) A self-supervised
pre-training strategy to enhance the feature representation of the coarse
generator. Our experiments with a public benchmark dataset of total-body PET
images show that SS-AEGAN consistently outperformed the state-of-the-art
synthesis methods with various dose reduction factors.