PET image harmonization using smoothing-cycleGAN

Abstract

Alzheimer’s disease (AD) is the most common cause of dementia. It is characterized by irreversible memory loss and degradation of cognitive skills. Amyloid PET imaging has been used in the diagnosis of AD to measure the amyloid burden in the brain. It is quantified by the Standard Uptake Value Ratio (SUVR). However, there is great variability in SUVR measurements when different scanner models are used. Therefore, standardization and harmonization is required for quantitative assessments of amyloid PET scans in a multi-center or longitudinal study. Conventionally, PET image harmonization has been tackled either by standardization protocols at the time of image reconstruction, or by applying a smoothing function to bring PET images to a common resolution using phantom data. In this work, we propose an automatic approach that aims to match the data distribution of PET images through unsupervised learning. To that end, we propose Smoothing-CycleGAN, a modified cycleGAN that uses a 3D smoothing kernel to learn the optimum Point Spread Function (PSF) for bringing PET images into a common spatial resolution. We validate our approach using two sets of datasets, and we analyze the SUVR agreement before and after PET image harmonization. Our results show that the PSF of PET images that have different spatial resolutions can be estimated automatically using Smoothing-cycleGAN, which results in better SUVR agreement after image translation.