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Author Notes:

Author to whom correspondence should be addressed. Xiaofeng Yang E-mail: xyang43@emory.edu; Telephone: (404)-778-8622; Fax: (404)-778-4139

The authors declare no conflict of interest.


Research Funding:

This research is supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718, the Department of Defense (DoD) Prostate Cancer Research Program (PCRP) Award W81XWH‐13‐1‐0269 and Dunwoody Golf Club Prostate Cancer Research Award, a philanthropic award provided by the Winship Cancer Institute of Emory University.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Radiology, Nuclear Medicine & Medical Imaging
  • contour delineation
  • dual energy CT
  • head-and-neck
  • radiation therapy
  • virtual monoenergetic image

Optimal virtual monoenergetic image in "TwinBeam" dual-energy CT for organs-at-risk delineation based on contrast-noise-ratio in head-and-neck radiotherapy


Journal Title:

Journal of Applied Clinical Medical Physics


Volume 20, Number 2


, Pages 121-128

Type of Work:

Article | Final Publisher PDF


Purpose: Dual-energy computed tomography (DECT) using TwinBeam CT (TBCT) is a new option for radiation oncology simulators. TBCT scanning provides virtual monoenergetic images which are attractive in treatment planning since lower energies offer better contrast for soft tissues, and higher energies reduce noise. A protocol is needed to achieve optimal performance of this feature. In this study, we investigated the TBCT scan schema with the head-and-neck radiotherapy workflow at our clinic and selected the optimal energy with best contrast-noise-ratio (CNR) in organs-at-risks (OARs) delineation for head-and-neck treatment planning. Methods and materials: We synthesized monochromatic images from 40 keV to 190 keV at 5 keV increments from data acquired by TBCT. We collected the Hounsfield unit (HU) numbers of OARs (brainstem, mandible, spinal cord, and parotid glands), the HU numbers of marginal regions outside OARs, and the noise levels for each monochromatic image. We then calculated the CNR for the different OARs at each energy level to generate a serial of spectral curves for each OAR. Based on these spectral curves of CNR, the mono-energy corresponding to the max CNR was identified for each OAR of each patient. Results: Computed tomography scans of ten patients by TBCT were used to test the optimal monoenergetic image for the CNR of OAR. Based on the maximized CNR, the optimal energy values were 78.5 ± 5.3 keV for the brainstem, 78.0 ± 4.2 keV for the mandible, 78.5 ± 5.7 keV for the parotid glands, and 78.5 ± 5.3 keV for the spinal cord. Overall, the optimal energy for the maximum CNR of these OARs in head-and-neck cancer patients was 80 keV. Conclusion: We have proposed a clinically feasible protocol that selects the optimal energy level of the virtual monoenergetic image in TBCT for OAR delineation based on the CNR in head-and-neck OAR. This protocol can be applied in TBCT simulation.

Copyright information:

© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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