Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging

TL;DR

This paper introduces PETNet, a graph convolutional neural network designed for analyzing PET images in Alzheimer's diagnosis, leveraging non-Euclidean graph structures to improve early detection accuracy.

Contribution

The paper presents a novel graph-based CNN architecture for 3D PET image analysis, capturing metabolic connectivity and disease progression patterns for better AD diagnosis.

Findings

PETNet outperforms existing deep learning methods on ADNI dataset.

Graph-based representation improves modeling of PET image signals.

Hierarchical graph clustering enhances feature extraction.

Abstract

Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET). In various literature it has been found that PET images can be better modeled as signals (e.g. uptake of florbetapir) defined on a network (non-Euclidean) structure which is governed by its underlying graph patterns of pathological progression and metabolic connectivity. In order to effectively apply deep learning framework for PET image analysis to overcome its limitation on Euclidean grid, we develop a solution for 3D PET image representation and analysis under a generalized, graph-based CNN architecture (PETNet), which analyzes PET signals defined on a group-wise inferred graph structure. Computations in PETNet are defined in non-Euclidean, graph (network) domain, as it performs feature extraction by convolution operations on spectral-filtered signals on the graph and pooling operations based on hierarchical graph clustering. Effectiveness of the PETNet is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which shows improved performance over both deep learning and other machine learning-based methods.