Automatic Generation of Lymphoma Post-Treatment PETs using Conditional-GANs

Abstract

Positron emission tomography (PET) imaging is a nuclear medicine functional imaging technique and as such it is expensive to perform and subjects the human body to radiation. Therefore, it would be ideal to find a technique that could allow for these images to be generated automatically. This generation can be done using deep learning techniques, more specifically with generative adversarial networks. As far as we are aware there have been no attempts at PET-to-PET generation to date. The objective of this article is to develop a generative adversarial network capable of generating after-treatment PET images from pre-treatment PET images. In order to develop this model, PET scans, originally in 3D, were converted to 2D images. Two methods were used, hand picking each slice and maximum intensity projection. After extracting the slices, several image co-registration techniques were applied in order to find which one would produce the best results according to two metrics, peak signal-to-noise ratio and structural similarity index. They achieved results of 18.8 and 0.856, respectively, using data from 90 patients with Hodgkin's Lymphoma.