Super-Energy-Resolution Material Decomposition for Spectral Photon-Counting CT Using Pixel-Wise Learning

Bingqing Xie,Loic Boussel,Pei Niu,Philippe Douek,Yuemin Zhu,Philippe Duvauchelle,Lihui Wang,Feng Yang,Pierre-Antoine Rodesch,Ting Su

doi:10.1109/access.2021.3134636

Abstract

Spectral photon-counting CT offers novel potentialities to achieve quantitative decomposition of material components, in comparison with traditional energy-integrating CT or dual-energy CT. Nonetheless, achieving accurate material decomposition, especially for low-concentration materials, is still extremely challenging for current sCT, due to restricted energy resolution stemming from the trade-off between the number of energy bins and undesired factors such as quantum noise.We propose to improve material decomposition by introducing the notion of super-energy-resolution in sCT. The super-energy-resolution material decomposition consists in learning the relationship between simulation and physical phantoms in image domain. To this end, a coupled dictionary learning method is utilized to learn such relationship in a pixel-wise way. The results on both physical phantoms and in vivo data showed that for the same decomposition method using lasso regularization, the proposed super-energy-resolution method achieves much higher decomposition accuracy and detection ability in contrast to traditional image-domain decomposition method using L1-norm regularization.

Highlights

S PECTRAL photon-counting X-ray computed tomography provides additional X-ray spectrum information about material components in an organ or tissue, due to its ability of separating photons into different energy bins by using photon-counting detector (PCD)
We propose to introduce the notion of super-energy-resolution (SER) into S PECTRAL photon-counting X-ray computed tomography (sCT) in order to improve the accuracy of material decomposition in image domain
The experiments were carried on real data from a Philips sCT prototype [21]–[23]

Summary

Introduction

S PECTRAL photon-counting X-ray computed tomography (sCT) provides additional X-ray spectrum information about material components in an organ or tissue, due to its ability of separating photons into different energy bins by using photon-counting detector (PCD). This ability makes possible efficient material decomposition of which the objective is to quantitatively measure different materials existed in a pixel. It is a huge challenge to obtain high accuracy of material decomposition in clinical applications, especially for low-concentration materials [1], [2] This is linked to the principle itself of sCT. More there are energy bins, the higher energy resolution (narrower energy-bin width) and more spectral

Results

Discussion

Conclusion