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Updated: May 1, 2026

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

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MGTP: Multi-Granularity Textual Prompts for Low-Dose Brain PET Image Denoising via Adversarial Diffusion Model.

Jiaqi Cui, Xinyi Zeng, Pinxian Zeng

IEEE Journal of Biomedical and Health Informatics

|October 27, 2025

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Positron Emission Tomography

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Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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This study introduces Multi-Granularity Textual Prompts (MGTP) to improve low-dose Positron Emission Tomography (PET) image denoising. The novel method integrates textual data with imaging, enhancing diagnostic accuracy and reducing radiation exposure concerns.

Area of Science:

Medical Imaging
Artificial Intelligence
Nuclear Medicine

Background:

Positron Emission Tomography (PET) is crucial in clinical settings but faces challenges with radiation risks at standard doses and poor quality at low doses.
Current low-dose PET denoising methods often neglect non-image textual data, leading to suboptimal results with lost context and detail.

Purpose of the Study:

To develop an advanced method for denoising low-dose PET images by integrating textual information.
To improve the quality and clinical utility of low-dose PET scans, thereby reducing radiation exposure.

Main Methods:

Proposed Multi-Granularity Textual Prompts (MGTP) using an adversarial diffusion model to denoise low-dose PET images.
Introduced a Cross-Modality Selective Conditioning (CMSC) module to harmonize image and multi-granularity textual prompts.

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Last Updated: May 1, 2026

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

Published on: August 14, 2019

Developed a Masked Prompt Reconstruction Network (MPR-Net) to preserve semantic and detailed information in denoised images.

Main Results:

The MGTP method effectively denoises low-dose PET images by incorporating diverse textual information.
The CMSC module successfully integrates semantic contexts and degradation details from textual prompts.
MPR-Net mitigated distortions, preserving crucial image semantics and details.

Conclusions:

The proposed MGTP method achieves state-of-the-art performance in low-dose PET image denoising.
Integrating multi-granularity textual prompts significantly enhances denoising quality compared to image-only methods.
This approach offers a promising solution for high-quality, low-dose PET imaging in clinical practice.