PET Image Super Resolution using Convolutional Neural Networks

摘要

Positron Emission Tomography (PET) is a nuclear, in-vivo medical imaging technology which can make 3D images of tissue metabolic act, in which high dose of tracer is needed to obtain a high quality PET image, which affects patients' health. Due to the limitations of using high dose tracer and limitations of physical imaging systems, it is not easy to get an image in desired resolution. Simplest approach to generate a high resolution image is by post processing. Single Image Super Resolution (SISR) is a post processing procedure to retrieve a high resolution image from a low resolution input. We propose a convolutional neural network trained on PET images which can estimate a high resolution PET image from its input low resolution image.