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Related Experiment Video

Updated: Oct 1, 2025

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

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Published on: August 6, 2013

Simultaneous Denoising of Dynamic PET Images Based on Deep Image Prior.

Cheng-Hsun Yang¹, Hsuan-Ming Huang²

¹Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd., Zhongzheng Dist., Taipei City 100, Taiwan.

Journal of Digital Imaging

|March 3, 2022

Summary

This summary is machine-generated.

We developed a novel deep image prior (DIP) method, called double DIP (DDIP), to denoise dynamic positron emission tomography (PET) images. DDIP significantly improves parametric image quality without requiring training data.

Keywords:

Deep image prior Dynamic PET Parametric imaging

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Area of Science:

Medical Imaging
Computational Imaging
Artificial Intelligence in Medicine

Background:

Parametric imaging from dynamic positron emission tomography (PET) data quantifies tracer kinetics.
High noise in pixel-wise time-activity curves degrades parametric image quality.

Purpose of the Study:

To introduce a novel unsupervised deep image prior (DIP) method for denoising dynamic PET images.
To enhance the quality of parametric PET images by reducing noise.

Main Methods:

Proposed a deep image prior (DIP) based unsupervised denoising method for dynamic PET data.
Introduced a modified version, double DIP (DDIP), utilizing two DIP architectures for improved input data generation.
Evaluated performance using computer simulations.

Main Results:

The DDIP method demonstrated superior performance compared to the single DIP method.
DDIP combined with data augmentation yielded higher quality PET parametric images than traditional filtering methods (e.g., non-local means, high constrained backprojection).

Conclusions:

The proposed DDIP method is an effective unsupervised approach for simultaneously denoising dynamic PET images.
DDIP offers a promising solution for improving the quality of quantitative PET parametric imaging.