Validation of deep learning-based nonspecific estimates for amyloid burden quantification with longitudinal data

Abstract

Purpose: To validate our previously proposed method of quantifying amyloid-beta (Aβ) load using nonspecific (NS) estimates generated with convolutional neural networks (CNNs) using [¹⁸F]Florbetapir scans from longitudinal and multicenter ADNI data.

Methods: 188 paired MR (T1-weighted and T2-weighted) and PET images were downloaded from the ADNI3 dataset, of which 49 subjects had 2 time-point scans. 40 Aβ- subjects with low specific uptake were selected for training. Multimodal ScaleNet (SN) and monomodal HighRes3DNet (HRN), using either T1-weighted or T2-weighted MR images as inputs) were trained to map structural MR to NS-PET images. The optimized SN and HRN networks were used to estimate the NS for all scans and then subtracted from SUVr images to determine the specific amyloid load (SAβ_L) images. The association of SAβ_L with various cognitive and functional test scores was evaluated using Spearman analysis, as well as the differences in SAβ_L with cognitive test scores for 49 subjects with 2 time-point scans and sensitivity analysis.

Results: SAβ_L derived from both SN and HRN showed higher association with memory-related cognitive test scores compared to SUVr. However, for longitudinal scans, only SAβ_L estimated from multimodal SN consistently performed better than SUVr for all memory-related cognitive test scores.

Conclusions: Our proposed method of quantifying Aβ load using NS estimated from CNN correlated better than SUVr with cognitive decline for both static and longitudinal data, and was able to estimate NS of [¹⁸F]Florbetapir. We suggest employing multimodal networks with both T1-weighted and T2-weighted MR images for better NS estimation.

Keywords: Alzheimer’s disease; Amyloid-Beta Positron Emission Tomography (Aβ-PET); Deep learning; Nonspecific uptake.