U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images

TL;DR

This paper introduces U-PET, a multi-task U-Net model that generates synthetic FDG-PET images from MRI scans and classifies dementia stages, improving early detection of Alzheimer's disease especially where PET scans are unavailable.

Contribution

The novel multi-task approach combines image synthesis and disease classification, enhancing interpretability and performance over baseline methods.

Findings

Successful generation of synthetic FDG-PET images

Improved classification accuracy for dementia stages

Visualization of relevant brain regions for diagnosis

Abstract

Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the combination of MRI and FDG-PET is the best image-based tool for diagnosis, FDG-PET is not always available. The reliable detection of Alzheimer's disease with only MRI could be beneficial, especially in regions where FDG-PET might not be affordable for all patients. To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classifies the dementia progression of the patient into cognitive normal (CN), cognitive impairment (MCI), and AD. The attention gates used in both task heads can visualize the most relevant parts of the brain, guiding the examiner and adding interpretability. Results show the successful generation of synthetic FDG-PET images and a performance increase in disease classification over the naive single-task baseline.