Removing Ring Artefacts for Photon-Counting Detectors Using Neural Networks in Different Domains
Wei Fang, Liang Li, Zhiqiang Chen
Abstract
The development of energy-resolving photon-counting detectors provides a new approach for obtaining spectral information in computed tomography. However, the responses of different photon counting detector pixels can be inconsistent, which will always cause stripe artefacts in projection domain and concentric ring artefacts in image domain. Traditional ring artifacts processing methods are mostly based on averaging and filtering. In this paper, we propose to use deep learning methods for ring artifacts removal respectively in image domain, projection domain and the polar coordinate system. Besides, by incorporating reconstruction process into neural networks, we unite the information from image domain and projection domain for ring artifacts removal under the framework of deep learning for the first time. A traditional ring artifacts removal method, which is based on wavelet and Fourier transform, is implemented for comparison. Quantitative analysis is performed on simulation and experimental results and it shows that deep learning based methods are promising in solving the problem of non-uniformity correction for photon-counting detectors.