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A Cross-modality Transformer Network for MR-guided Low-dose Tau PET Image Denoising.

Se-In Jang¹, Cristina Lois Gomez², Alex Becker²

¹Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06510 USA.

IEEE Transactions on Radiation and Plasma Medical Sciences

|February 16, 2026

Summary

This study introduces a new deep learning method for low-dose tau PET imaging in Alzheimer's disease. The approach enhances image quality by integrating PET and MR data, improving diagnostic accuracy for dementia monitoring.

Keywords:

Cross-modality Deep Learning Low-dose Self-attention Tau PET Imaging Transformer

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Area of Science:

Neuroimaging
Artificial Intelligence in Medicine
Radiochemistry

Background:

Tau PET imaging is crucial for diagnosing and monitoring Alzheimer's disease and related dementias.
Reducing injected radiation dose in tau PET is vital for longitudinal studies.
Current methods for low-dose PET imaging require further improvement.

Purpose of the Study:

To develop a novel deep learning approach for improving low-dose tau PET imaging.
To integrate PET and MR prior information using cross-modality transformers.
To enhance the quality of tau PET images for better disease monitoring.

Main Methods:

A novel deep learning network incorporating cross-modality transformer blocks was developed.
The method integrates spatial and channel information using self-attention maps.
Performance was evaluated on 139 dynamic 18F-MK-6240 tau PET datasets (early and late frames).

Main Results:

The proposed deep learning network significantly improved low-dose tau PET imaging quality.
The method outperformed reference networks that concatenated PET and MR images.
Integration of PET and MR priors via cross-modality attention enhanced image reconstruction.

Conclusions:

The developed deep learning approach effectively improves low-dose tau PET imaging.
This method holds promise for accurate, longitudinal monitoring of Alzheimer's disease progression.
Integrating multi-modal imaging data with advanced AI techniques is beneficial for dementia research.