Algorithm of ICA-based poisson-noise reduction and its application to CT imaging

Abstract

CT (computed tomography) imaging is a technology which uses X-ray beams (radiation) and computers to form detailed, cross-sectional images of an area of anatomy. However, the random scattered X-ray in CT imaging system will reduce radiographic contrast greatly in CT images. In this paper, a four-step method is proposed for decoding CT images: first, the EGSnrc Monte Carlo simulation system is used to simulate CT imaging and simulated data will be validated by real experimental data in the same experimental conditions; second, scattered X-ray image simulated by EGSnrc will be transformed into ICA-domain (independent component analysis-domain) to obtain the main magnitude of scattering data; third, a noise-reduction algorithm based on ICA-domain shrinkage is applied to smooth the CT image; fourth, the conventional linear deconvolution follows. The simulation results show that the reconstructed image is dramatically improved in comparison to that without the noise-removing filters, and the proposed method is also applied to real experimental X-ray imaging.