Segmentation-free x-ray energy spectrum estimation for computed tomography

Wei Zhao; Qiude Zhang; Tianye Niu

doi:10.1117/12.2216196

22 March 2016 Segmentation-free x-ray energy spectrum estimation for computed tomography

Wei Zhao, Qiude Zhang, Tianye Niu

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 978339 (2016) https://doi.org/10.1117/12.2216196
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

X-ray energy spectrum plays an essential role in imaging and related tasks. Due to the high photon flux of clinical CT scanners, most of the spectrum estimation methods are indirect and are usually suffered from various limitations. The recently proposed indirect transmission measurement-based method requires at least the segmentation of one material, which is insufficient for CT images of highly noisy and with artifacts. To combat for the bottleneck of spectrum estimation using segmented CT images, in this study, we develop a segmentation-free indirect transmission measurement based energy spectrum estimation method using dual-energy material decomposition. The general principle of the method is to compare polychromatic forward projection with raw projection to calibrate a set of unknown weights which are used to express the unknown spectrum together with a set of model spectra. After applying dual-energy material decomposition using high-and low-energy raw projection data, polychromatic forward projection is conducted on material-specific images. The unknown weights are then iteratively updated to minimize the difference between the raw projection and estimated projection. Both numerical simulations and experimental head phantom are used to evaluate the proposed method. The results indicate that the method provides accurate estimate of the spectrum and it may be attractive for dose calculations, artifacts correction and other clinical applications.

Citation Download Citation

Wei Zhao, Qiude Zhang, and Tianye Niu "Segmentation-free x-ray energy spectrum estimation for computed tomography", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 978339 (22 March 2016); https://doi.org/10.1117/12.2216196

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