Poisson diffusion probabilistic model for low-dose SPECT sinogram denoising
Abstract
BACKGROUND: Low-dose single photon emission computed tomography (SPECT) sinograms often suffer from noise due to photon attenuation during the imaging process. As a result, developing effective denoising methods for low-dose SPECT images has become an essential research topic. Traditional image denoising methods struggle to balance noise reduction with the preservation of important image details, especially in medical applications where accurate image structures are critical.
PURPOSE: This paper proposes a diffusion probabilistic model based on Poisson noise, named the Poisson diffusion probabilistic model (PDPM), for denoising low-dose SPECT sinograms. Considering the physical principles behind the formation of low-dose SPECT sinograms, PDPM replaces the Gaussian noise traditionally used in diffusion models with Poisson noise, utilizing low-dose and normal-dose SPECT sinograms as the starting and ending points of the denoising process, respectively.
METHODS: We present a preliminary framework for PDPM that encompasses both the forward and reverse processes. Subsequently, we refine this preliminary framework by implementing two improvements: discarding the forward process and generating the training dataset using a method based on the ideal reverse process, as well as introducing our proposed Temporal Prediction Aggregation Module (TPAM) into the reverse process to enhance the model's image denoising performance.
RESULTS: Experiments conducted on the simulated SPECT dataset…
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