Detecting Beta-Amyloid Plaque via Low Rank Based Orthogonal Projection and Spatial-Spectrum Detector Using High-Resolution Quantitative Susceptibility Mapping for Preclinical Studies.

Detecting beta-amyloid (A$\beta$) plaques at different stages is crucial for accurate assessment and effective intervention in Alzheimer's disease (AD). In this study, we developed a novel method for reliably identifying A$\beta$ plaques, characterized by sparse negative susceptibility values, in preclinical studies using high-resolution quantitative susceptibility mapping (QSM), named QSM-PLAQUE A$\beta$ Detector. This approach decomposes a high-resolution QSM MRI image into three components: L (representing the background subspace), S (representing the signals subspace), and N (representing the noise). Subsequently, we established an orthogonal subspace based on L to eliminate the background from the sum of L and S. Finally, a plaque detection process was conducted, where A$\beta$ plaques were identified based on the neighbor spectrum (NS) of a voxel being tested rather than just analyzing the voxel itself alone. Experiments demonstrated that the proposed method effectively detects A$\beta$ plaques of varying shapes and intensities across the entire mouse brain. It shows robust performance across histology, high-resolution QSM MRI, and synthesized datasets, without requiring training samples. The QSM-PLAQUE A$\beta$ Detector provides a practical framework for identifying and visualizing A$\beta$ plaques in preclinical studies, offering a new strategy for quantitative assessment of A$\beta$ plaques and may guide the development of advanced techniques for preclinical AD research.