Sinogram Denoise Based on Generative Adversarial Networks

TL;DR

This paper introduces a GAN-based method for denoising sinogram data in SPECT imaging, demonstrating significant noise reduction and improved image reconstruction quality on both simulated and real-world data.

Contribution

The paper presents a novel GAN-based approach specifically designed for sinogram denoising in SPECT imaging, enhancing reconstruction quality over traditional methods.

Findings

Significant noise reduction in sinograms

Improved image reconstruction quality

Effective on both simulated and real-world data

Abstract

A novel method for sinogram denoise based on Generative Adversarial Networks (GANs) in the field of SPECT imaging is presented. Projection data from software phantoms were used to train the proposed model. For evaluation of the efficacy of the method Shepp Logan based phantom, with various noise levels added where used. The resulting denoised sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original noised sinograms. As the results show, the proposed method significantly denoise the sinograms and significantly improves the reconstructions. Finally, to demonstrate the efficacy and capability of the proposed method results from real-world DAT-SPECT sinograms are presented.