Automated, patient-specific estimation of regional imparted energy and dose from tube current modulated computed tomography exams across 13 protocols

Jeremiah Sanders; Xiaoyu Tian; William  P. Segars; John Boone; Ehsan Samei

doi:10.1117/1.JMI.4.1.013503

24 January 2017 Automated, patient-specific estimation of regional imparted energy and dose from tube current modulated computed tomography exams across 13 protocols

Jeremiah Sanders, Xiaoyu Tian, William P. Segars, John Boone, Ehsan Samei

Author Affiliations +

Journal of Medical Imaging, Vol. 4, Issue 1, 013503 (January 2017). https://doi.org/10.1117/1.JMI.4.1.013503

Abstract

Currently, computed tomography (CT) dosimetry relies on surrogates for dose, such as CT dose index and size-specific dose estimates, rather than dose per se. Organ dose is considered as the gold standard for radiation dosimetry. However, organ dose estimation requires precise knowledge of organ locations. Regional imparted energy and dose can also be used to quantify radiation burden and are beneficial because they do not require knowledge of organ size or location. This work investigated an automated technique to retrospectively estimate the imparted energy from tube current-modulated (TCM) CT exams across 13 protocols. Monte Carlo simulations of various head and body TCM CT examinations across various tube potentials and TCM strengths were performed on 58 adult computational extended cardiac-torso phantoms to develop relationships between scanned mass and imparted energy normalized by dose length product. Results from the Monte Carlo simulations indicate that normalized imparted energy increases with increasing both scanned mass and tube potential, but it is relatively unaffected by the strength of the TCM. The automated algorithm was tested on 40 clinical datasets with a 98% success rate.

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Jeremiah Sanders, Xiaoyu Tian, William P. Segars, John Boone, and Ehsan Samei "Automated, patient-specific estimation of regional imparted energy and dose from tube current modulated computed tomography exams across 13 protocols," Journal of Medical Imaging 4(1), 013503 (24 January 2017). https://doi.org/10.1117/1.JMI.4.1.013503

Received: 27 April 2016; Accepted: 29 December 2016; Published: 24 January 2017

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