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PET image denoising based on denoising diffusion probabilistic model

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic model (DDPM) was a distribution learning-based model, which tried to transform a normal distribution into a specific data distribution based on iterative refinements. In this work, we proposed and evaluated different DDPM-based methods for PET image denoising.

Methods

Under the DDPM framework, one way to perform PET image denoising was to provide the PET image and/or the prior image as the input. Another way was to supply the prior image as the network input with the PET image included in the refinement steps, which could fit for scenarios of different noise levels. 150 brain [\(^{18}\)F]FDG datasets and 140 brain [\(^{18}\)F]MK-6240 (imaging neurofibrillary tangles deposition) datasets were utilized to evaluate the proposed DDPM-based methods.

Results

Quantification showed that the DDPM-based frameworks with PET information included generated better results than the nonlocal mean, Unet and generative adversarial network (GAN)-based denoising methods. Adding additional MR prior in the model helped achieved better performance and further reduced the uncertainty during image denoising. Solely relying on MR prior while ignoring the PET information resulted in large bias. Regional and surface quantification showed that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference achieved the best performance.

Conclusion

DDPM-based PET image denoising is a flexible framework, which can efficiently utilize prior information and achieve better performance than the nonlocal mean, Unet and GAN-based denoising methods.

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Funding

This work was supported by the National Institutes of Health under grants R21AG067422, R03EB030280, R01AG078250, P41EB022544 and P01AG036694.

Author information

Authors and Affiliations

  1. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, 32611, FL, USA

    Kuang Gong

  2. Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, 02114, MA, USA

    Kuang Gong, Keith Johnson & Georges El Fakhri

  3. Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, 02114, MA, USA

    Kuang Gong & Quanzheng Li

  4. Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, 77030, TX, USA

    Tinsu Pan

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  1. Kuang Gong
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Correspondence to Kuang Gong.

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Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

For the [\(^{18}\)F]FDG datasets, informed consent was waived due to the retrospective merits of the datasets. For the [\(^{18}\)F]MK-6240 datasets, informed consent was obtained from all the participants.

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The authors declare no competing interests.

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Gong, K., Johnson, K., El Fakhri, G. et al. PET image denoising based on denoising diffusion probabilistic model. Eur J Nucl Med Mol Imaging 51, 358–368 (2024). https://doi.org/10.1007/s00259-023-06417-8

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  • DOI: https://doi.org/10.1007/s00259-023-06417-8

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