Population and individual information guided PET image denoising using deep neural network

Abstract

Positron emission tomography (PET) images still suffer from low signal-to-noise ratio (SNR) due to various physical degradation factors. Recently deep neural networks (DNNs) have been successfully applied to medical image denoising tasks when large number of training pairs are available. Previously the deep image prior framework1 shows that individual information can be enough to train a denoising network, with noisy image itself as the training label. In this work, we propose to improve PET image quality by jointly employing population and individual information based on DNN. The population information was utilized by pre-training the network using a group of patients. The individual information was introduced during testing phase by fine-tuning the population-information-trained network. Unlike traditional DNN denoising, in this framework fine-tuning during testing phase is available as the noisy PET image itself was treated as the training label. Quantification results based on clinical PET/MR datasets containing thirty patients demonstrate that the proposed framework outperforms Gaussian, non-local mean and deep image prior denoising methods.