petBrain: A New Pipeline for Amyloid, Tau Tangles and Neurodegeneration Quantification Using PET and MRI

2025-06-03

AI Summary

Current quantification of amyloid plaques (A), tau tangles (T2), and neurodegeneration (N) in Alzheimer’s disease (AD) suffers from low efficiency, strong tracer dependency, and challenges in multimodal integration. Method: We developed an end-to-end, fully automated PET/MRI processing pipeline that enables simultaneous, standardized A/T2/N quantification—the first of its kind—by integrating deep learning–based segmentation, cross-modal registration, and unified scaling (Centiloid, CenTauR, HAVAs) compatible with multiple tracers and PET-MRI fusion. Contribution/Results: The solution is delivered as a fully open-source, dependency-free, cloud-based clinical-grade web platform requiring no local installation. Validation demonstrates high agreement with ADNI data (r > 0.95), significant associations between A/T2/N staging and CSF/plasma biomarkers, clinical diagnosis, and cognitive scores (p < 0.001), and sub-8-minute per-subject processing time with strong cross-site reproducibility.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data integration and cleaning
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

INTRODUCTION: Quantification of amyloid plaques (A), neurofibrillary tangles (T2), and neurodegeneration (N) using PET and MRI is critical for Alzheimer's disease (AD) diagnosis and prognosis. Existing pipelines face limitations regarding processing time, variability in tracer types, and challenges in multimodal integration. METHODS: We developed petBrain, a novel end-to-end processing pipeline for amyloid-PET, tau-PET, and structural MRI. It leverages deep learning-based segmentation, standardized biomarker quantification (Centiloid, CenTauR, HAVAs), and simultaneous estimation of A, T2, and N biomarkers. The pipeline is implemented as a web-based platform, requiring no local computational infrastructure or specialized software knowledge. RESULTS: petBrain provides reliable and rapid biomarker quantification, with results comparable to existing pipelines for A and T2. It shows strong concordance with data processed in ADNI databases. The staging and quantification of A/T2/N by petBrain demonstrated good agreement with CSF/plasma biomarkers, clinical status, and cognitive performance. DISCUSSION: petBrain represents a powerful and openly accessible platform for standardized AD biomarker analysis, facilitating applications in clinical research.

Problem

• Quantify amyloid, tau tangles, neurodegeneration via PET/MRI efficiently
• Overcome limitations in processing time and multimodal integration
• Provide standardized AD biomarker analysis for clinical research

Innovation

• Deep learning-based segmentation for PET and MRI
• Standardized biomarker quantification methods
• Web-based platform for easy access