PET Image Denoising Using Deep Neural Network

Abstract

Deep neural networks have been widely and successfully used in computer vision and attracted growing interests in medical imaging. In this work, we trained a deep residual convolutional neural network to improve quality of PET images. To train the deep neural network, we augmented real patient data with computer simulated phantom data. Specifically, we first trained the network using simulation data and then fine tuned the network using real data. Results based on simulation and real data show that the proposed method is more effective in removing noise than the traditional Gaussian filtering method.