Improved Image Reconstruction Using Multi-Energy Information in Spectral Photon-Counting CT

Abstract

Spectral photon-counting computed tomography (sCT) appears as a promising imaging technique for clinical applications thanks to its ability to offer low dose and possibility of quantitatively analyzing the composition of materials in a pixel. However, due to the dispatching of photons into different energy bins, the quality of sCT image at each energy bin is considerably degraded. We propose a reconstruction method for sCT images by combining multi-energy information. The method is based on clustering pixels containing similar material compositions, performing linear fitting within each class for all the energy images two-by-two, projecting the pixel values of the images at other energy bins to the pixel of the image at the current energy bin, and combining the original pixel value and projected pixel values. The results on both simulation and real data demonstrated the effectiveness of the proposed method, in terms of both image reconstruction quality and material decomposition accuracy.

Keywords

• Image reconstruction
• multi-energy information
• data clustering
• linear fitting
• projection data
• denoising