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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Image quality restoration in 15-s breath-hold PET using a diffusion-based neural network.

Jingxi Hu^1,2,3, Lianghua Li⁴, Qiyang Zhang^1,2

¹Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Medical Physics

|February 28, 2026

Summary

This study introduces TAM-DiffPET, a novel deep learning framework that significantly reduces noise in short, breath-hold PET scans. The method enhances lesion visibility and maintains quantitative accuracy for improved diagnostic potential.

Keywords:

PET image enhancement Positron Emission Tomography breath‐hold PET deep learning

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Simultaneous PET/MRI Imaging During Mouse Cerebral Hypoxia-ischemia

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Published on: September 20, 2015

Area of Science:

Medical Imaging
Artificial Intelligence
Radiochemistry

Background:

Breath-hold PET imaging reduces motion artifacts but suffers from noise and poor lesion detection due to short acquisition times.
Improving image quality in ultrashort PET scans is a significant technical challenge for clinical applications.

Purpose of the Study:

To develop a deep learning framework using a diffusion probabilistic model to enhance image quality in 15-second breath-hold PET scans.
The goal is to suppress noise and improve lesion visibility while preserving quantitative accuracy.

Main Methods:

A denoising diffusion probabilistic model (DDPM) with Temporal Attention Modulation (TAM) was developed, named TAM-DiffPET.
The model was trained on paired 15-s breath-hold and 5-min free-breathing PET scans from 230 patients.
Performance was evaluated using PSNR, SSIM, and SUV error analysis, compared against U-Net, CycleGAN, and vanilla DDPM.

Main Results:

TAM-DiffPET achieved superior performance with the highest PSNR (40.2 dB) and SSIM (0.995) compared to other deep learning methods.
Quantitative analysis showed lower SUV error within lesion regions, and visual assessment revealed enhanced lesion contrast and reduced noise.
Difference maps and SUV distribution analysis confirmed minimal deviation from reference scans and preserved tracer uptake consistency.

Conclusions:

The proposed diffusion-based framework effectively denoises ultrashort breath-hold PET images, improving visual clarity and quantitative accuracy.
This technology holds potential for clinical use in motion-prone imaging, enhancing diagnostic accuracy while reducing scan time and radiation exposure.