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.
Deep neural networks have attracted growing interests in medical image due to its success in computer vision tasks. One barrier for the application of deep neural networks is the need of large amounts of prior training pairs, which is not always feasible in clinical practice. Recently, the deep image prior framework shows that the convolutional neural network (CNN) can learn intrinsic structure information from the corrupted image. In this work, an iterative parametric reconstruction framework is proposed using deep neural network as constraint. The network does not need prior training pairs, but only the patient’s own CT image. The training is based on Logan plot derived from multi-bed-position dynamic positron emission tomography (PET) images using 68Ga-PRGD2 tracer. We formulated the estimation of the slope of Logan plot as a constraint optimization problem and solved it using the alternating direction method of multipliers (ADMM) algorithm. Quantification results based on real patient dataset shows that the proposed parametric reconstruction method is better than the Gaussian denoising and non-local mean denoising methods.