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Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model by Pan, Shaoyan; Abouei, Elham; Peng, Junbo; Qian, Joshua; Wynne, Jacob F; Wang, Tonghe; Chang, Chih-Wei; Roper, Justin; Nye, Jonathon A; Mao, Hui; Yang, Xiaofeng is a scholarly article available to read on EtoBox.

What is Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Model about?

Objective: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. Approach: We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Results: In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278+/-0.122%, PSNR of 33.783+/

Author

Pan, Shaoyan; Abouei, Elham; Peng, Junbo; Qian, Joshua; Wynne, Jacob F; Wang, Tonghe; Chang, Chih-Wei; Roper, Justin; Nye, Jonathon A; Mao, Hui; Yang, Xiaofeng

Published

2023

Language

EN