Paper
16 March 2011 The feasibility of universal DLP-to-risk conversion coefficients for body CT protocols
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Abstract
The effective dose associated with computed tomography (CT) examinations is often estimated from dose-length product (DLP) using scanner-independent conversion coefficients. Such conversion coefficients are available for a small number of examinations, each covering an entire region of the body (e.g., head, neck, chest, abdomen and/or pelvis). Similar conversion coefficients, however, do not exist for examinations that cover a single organ or a sub-region of the body, as in the case of a multi-phase liver examination. In this study, we extended the DLP-to-effective dose conversion coefficient (k factor) to a wide range of body CT protocols and derived the corresponding DLP-to-cancer risk conversion coefficient (q factor). An extended cardiactorso (XCAT) computational model was used, which represented a reference adult male patient. A range of body CT protocols used in clinical practice were categorized based on anatomical regions examined into 10 protocol classes. A validated Monte Carlo program was used to estimate the organ dose associated with each protocol class. Assuming the reference model to be 20 years old, effective dose and risk index (an index of the total risk for cancer incidence) were then calculated and normalized by DLP to obtain the k and q factors. The k and q factors varied across protocol classes; the coefficients of variation were 28% and 9%, respectively. The small variation exhibited by the q factor suggested the feasibility of universal q factors for a wide range of body CT protocols.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiang Li, Ehsan Samei, W. Paul Segars, Erik K. Paulson, and Donald P. Frush "The feasibility of universal DLP-to-risk conversion coefficients for body CT protocols", Proc. SPIE 7961, Medical Imaging 2011: Physics of Medical Imaging, 79612A (16 March 2011); https://doi.org/10.1117/12.878616
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Cited by 4 scholarly publications.
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KEYWORDS
Liver

Cancer

Computed tomography

Monte Carlo methods

Chest

Abdomen

Digital Light Processing

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