Dual Prompting for Diverse Count-level PET Denoising
Journal:
arXiv
Published Date:
May 5, 2025
Abstract
The to-be-denoised positron emission tomography (PET) volumes are inherent
with diverse count levels, which imposes challenges for a unified model to
tackle varied cases. In this work, we resort to the recently flourished prompt
learning to achieve generalizable PET denoising with different count levels.
Specifically, we propose dual prompts to guide the PET denoising in a
divide-and-conquer manner, i.e., an explicitly count-level prompt to provide
the specific prior information and an implicitly general denoising prompt to
encode the essential PET denoising knowledge. Then, a novel prompt fusion
module is developed to unify the heterogeneous prompts, followed by a
prompt-feature interaction module to inject prompts into the features. The
prompts are able to dynamically guide the noise-conditioned denoising process.
Therefore, we are able to efficiently train a unified denoising model for
various count levels, and deploy it to different cases with personalized
prompts. We evaluated on 1940 low-count PET 3D volumes with uniformly randomly
selected 13-22\% fractions of events from 97 $^{18}$F-MK6240 tau PET studies.
It shows our dual prompting can largely improve the performance with informed
count-level and outperform the count-conditional model.
