Segmentation-free x-ray energy spectrum estimation for computed tomography using dual-energy material decomposition

Wei Zhao; Lei Xing; Qiude Zhang; Qingguo Xie; Tianye Niu

doi:10.1117/1.JMI.4.2.023506

30 June 2017 Segmentation-free x-ray energy spectrum estimation for computed tomography using dual-energy material decomposition

Wei Zhao, Lei Xing, Qiude Zhang, Qingguo Xie, Tianye Niu

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 2, 023506 (June 2017). https://doi.org/10.1117/1.JMI.4.2.023506

Abstract

An x-ray energy spectrum plays an essential role in computed tomography (CT) imaging and related tasks. Because of the high photon flux of clinical CT scanners, most of the spectrum estimation methods are indirect and usually suffer from various limitations. In this study, we aim to provide a segmentation-free, indirect transmission measurement–based energy spectrum estimation method using dual-energy material decomposition. The general principle of this method is to minimize the quadratic error between the polychromatic forward projection and the raw projection to calibrate a set of unknown weights, which are used to express the unknown spectrum together with a set of model spectra. The polychromatic forward projection is performed using material-specific images, which are obtained using dual-energy material decomposition. The algorithm was evaluated using numerical simulations, experimental phantom data, and realistic patient data. The results show that the estimated spectrum matches the reference spectrum quite well and the method is robust. Extensive studies suggest that the method provides an accurate estimate of the CT spectrum without dedicated physical phantom and prolonged workflow. This paper may be attractive for CT dose calculation, artifacts reduction, polychromatic image reconstruction, and other spectrum-involved CT applications.

Citation Download Citation

Wei Zhao, Lei Xing, Qiude Zhang, Qingguo Xie, and Tianye Niu "Segmentation-free x-ray energy spectrum estimation for computed tomography using dual-energy material decomposition," Journal of Medical Imaging 4(2), 023506 (30 June 2017). https://doi.org/10.1117/1.JMI.4.2.023506

Received: 8 November 2016; Accepted: 9 June 2017; Published: 30 June 2017

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