Deep convolutional networks for PET super-resolution

Abstract

Positron emission tomography (PET) provides valuable functional information that is widely used in clinical domains such as oncology and neurology. However, the structural quality of PET images may not be sufficient to effectively evaluate small regions of interest. Image super-resolution techniques aim to recover a high-resolution image from an input low-resolution version. We study adaptations of deep convolutional neural network architectures for improving the spatial resolution of PET images. The proposed super-resolution model involves a deep architecture that uses convolutional blocks together with various residual connections for more effective and efficient training. We use the supervised setting where the downscaled versions of the original PET images are given as the low-resolution input to the deep networks and the original images are used as the high-resolution target data to be recovered. Experiments show that the proposed model performs better than a multi-scale convolutional architecture according to both quantitative performance metrics and visual qualitative evaluation.