Presentation + Paper
9 March 2017 An atlas-based organ dose estimator for tomosynthesis and radiography
Author Affiliations +
Abstract
The purpose of this study was to provide patient-specific organ dose estimation based on an atlas of human models for twenty tomosynthesis and radiography protocols. The study utilized a library of 54 adult computational phantoms (age: 18-78 years, weight 52-117 kg) and a validated Monte-Carlo simulation (PENELOPE) of a tomosynthesis and radiography system to estimate organ dose. Positioning of patient anatomy was based on radiographic positioning handbooks. The field of view for each exam was calculated to include relevant organs per protocol. Through simulations, the energy deposited in each organ was binned to estimate normalized organ doses into a reference database. The database can be used as the basis to devise a dose calculator to predict patient-specific organ dose values based on kVp, mAs, exposure in air, and patient habitus for a given protocol. As an example of the utility of this tool, dose to an organ was studied as a function of average patient thickness in the field of view for a given exam and as a function of Body Mass Index (BMI). For tomosynthesis, organ doses can also be studied as a function of x-ray tube position. This work developed comprehensive information for organ dose dependencies across tomosynthesis and radiography. There was a general exponential decrease dependency with increasing patient size that is highly protocol dependent. There was a wide range of variability in organ dose across the patient population, which needs to be incorporated in the metrology of organ dose.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jocelyn Hoye, Yakun Zhang, Greeshma Agasthya, Greg Sturgeon, Anuj Kapadia, W. Paul Segars, and Ehsan Samei "An atlas-based organ dose estimator for tomosynthesis and radiography", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 1013204 (9 March 2017); https://doi.org/10.1117/12.2255583
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KEYWORDS
Radiography

Spine

Bone

Heart

Liver

Lung

Monte Carlo methods

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