Deep learning-based estimation of non-specific uptake in amyloid-pet images from structural mri for improved quantification of amyloid load in alzheimer's disease

Methods like PET imaging which use radiotracers that bind to amyloid-β (Aβ) - plaques that accumulate and lead to Alzheimer's disease (AD) - are crucial to detect AD early. However, current semi-quantitative methods that quantify Aβ using the Standardized Uptake Values Ratio (SUVr) cannot distinguish between the specific binding of radiotracers to Aβ and non-specific binding to other targets. In this paper, we propose a novel method to predict non-specific binding from MR images using deep learning by developing a novel conditional Generative Adversarial Network (cGAN) to improve Aβ quantification. PET images with low amyloid load were selected to represent non-specific uptake of the Aβ [11C]PiB radiotracer. Paired MR and PET images with low amyloid load were used for training, with the MRI image as the network input and the PET image as the training label. The cGAN was trained to learn the mapping between the MR image and the non-specific PET image. The generated non-specific PET images were compared to the real PET scans, achieving a mean SUVr difference of 1.90% in the gray matter. The small SUVr difference indicated that we have successfully developed cGAN for predicting the non-specific uptake of PET and that deep learning can be used to estimate the non-specific binding of Aβ PET radiotracer from MR images.