A simulation study on photon-counting denoising based on subspace decomposition

Abstract

Photon counting detector (PCD) is a hot topic at present. Compared with traditional energy integral detector, it has the potential of high spatial resolution, high sensitivity and low dose, which can effectively promote medical imaging diagnosis. However, when PCD is counting X-ray photons, the photon number of each energy bin is relatively small. Additionally, charge-sharing response and pulse superposition effect will also affect the photon count rate, resulting in serious noise and affecting the imaging quality. In this paper, a photon-counting denoising algorithm based on subspace decomposition is proposed. According to the similarity between the data of different bins and the self-similarity of the data, this paper constructs sparse representation by subspace decomposition method and uses block matching algorithm to suppress noise. In simulation experiments, we carried out spectral computed tomography imaging experiments with the three-dimensional phantom of a digital mice based on PCD, and denoised the data by different algorithms. The quantitative results show that our method improves peak signal-to-noise ratio by 2.21dB compared with block-matching and 3D filtering when photon flux is 4×103 , which verifies the potential of the proposed algorithm in medical imaging.