MR guided PET image denoising  based on denoising diffusion probabilistic model and data consistency constraint

Kuang Gong

doi:10.1117/12.2653704

7 April 2023 MR guided PET image denoising based on denoising diffusion probabilistic model and data consistency constraint

Kuang Gong

Author Affiliations +

Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 124631G (2023) https://doi.org/10.1117/12.2653704
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Due to various physical degradation factors, the signal-to-noise ratio (SNR) and image quality of PET needs further improvements. In this work, we proposed a denoising diffusion probabilistic model-based framework for PET image denoising, where the MR prior image was supplied as the additional network input, and the PET information was included in the iterative refinement steps based on Gaussian distribution assumption. 140 ¹⁸F-MK- 6240 datasets were used in the evaluation, with 1/4 and 1/8 low-dose levels tested for different methods. Global and regional quantifications show that the proposed framework can outperform the Unet-based denoising and MR-guided nonlocal mean denoising methods.

Conference Presentation

Citation Download Citation

Kuang Gong "MR guided PET image denoising based on denoising diffusion probabilistic model and data consistency constraint", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 124631G (7 April 2023); https://doi.org/10.1117/12.2653704

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