Cited by 2Advanced MRI Techniques and ApplicationsDementia and Cognitive Impairment ResearchMedical Image Segmentation Techniques

Detecting beta‐amyloid plaque via low rank based orthogonal projection and spatial‐spectrum detector using high‐resolution quantitative susceptibility mapping for preclinical studies

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Chen, Jie, Han, Xinyue, Liu, Zhuoheng, Zhou, Chengqian, Hu, Rui, Tabassam, Saira, Wyatt-Johnson, Season K., Oblak, Adrian L. +3 more·2026·IEEE Transactions on Biomedical Engineering

Abstract

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.

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Crossref · 10.1109/tbme.2025.3614233

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  • Aβ-plaque locations in 5xFAD mice were identified by the QSM-Plaque Aβ Detector, which was developed previously with reported precision of 0.86 and recall of 0.94.

    “We identified Aβ‐plaque locations in 5xFAD mice by the QSM‐Plaque Aβ Detector which was developed previously with reported precision of 0.86 and recall of 0.94”

    From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [59]

    The Dataset Description identifies 5xFAD mice, and the High-resolution QSM Slice Results describe plaque locations detected by the QSM-PLAQUE Aβ Detector. The paper reports evaluating precision and recall, but the passages in our copy do not establish the claimed values of 0.86 and 0.94; searches for both numbers did not locate them. Page numbers were not available in the indexed passages. (LLM, conf=0.82)

    Experimental — accuracy not yet measured
  • QSM showed a shift toward more diamagnetic susceptibility in plaque regions.

    “QSM showed a shift toward more diamagnetic susceptibility, consistent with altered plaque composition and local susceptibility distribution. 32 , 59”

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Research works printed but not linked: 10

  1. [2]Yoon H, “ Automatic detection of amyloid beta plaques in somatosensory cortex of an alzheimer’s disease mouse using deep learning,” IEEE Access, vol. 9, pp. 161 926– 161 936, 2021.
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Authors
Chen, J., Han, X., Liu, Z., Zhou, C., Hu, R., Tabassam, S., Wyatt-Johnson, S. K., Oblak, A. L., Brutkiewicz, R. R., Lin, M., & Wang, N.
Affiliations
  • The University of Texas Southwestern Medical Center (US)
  • McGill University (CA)
  • Department of Microbiology and Immunology, Indiana University School of Medicine, USA
  • Indiana University Indianapolis (US)
  • University of Minnesota (US)
Venue
IEEE Transactions on Biomedical Engineering
Published
2026-05
Volume
73
Issue
5
Pages
1734-1745
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
OpenAlex
W4414462989
Cited by
2
References
75
Citations by year
2026: 2
FWCI
1.15
Type
journal article
Language
en
Open access
Locations
  • IEEE Transactions on Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion)
Funders
National Institutes of Health (R01 NS125020)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
amyloid plaques, Alzheimer's disease, quantitative susceptibility mapping, plaque detection, low-rank decomposition, orthogonal projection, small animal imaging, mouse brain, unsupervised segmentation
MeSH terms
Algorithms, Alzheimer Disease, Animals, Brain, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Mice, Transgenic, Amyloid beta-Peptides, Mice, Plaque, Amyloid

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