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Proceedings Paper

Towards a comprehensive CT image segmentation for thoracic organ radiation dose estimation and reporting
Author(s): Cristian Lorenz; Heike Ruppertshofen; Torbjörn Vik; Peter Prinsen; Jens Wiegert
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Paper Abstract

Administered dose of ionizing radiation during medical imaging is an issue of increasing concern for the patient, for the clinical community, and for respective regulatory bodies. CT radiation dose is currently estimated based on a set of very simplifying assumptions which do not take the actual body geometry and organ specific doses into account. This makes it very difficult to accurately report imaging related administered dose and to track it for different organs over the life of the patient. In this paper this deficit is addressed in a two-fold way. In a first step, the absorbed radiation dose in each image voxel is estimated based on a Monte-Carlo simulation of X-ray absorption and scattering. In a second step, the image is segmented into tissue types with different radio sensitivity. In combination this allows to calculate the effective dose as a weighted sum of the individual organ doses. The main purpose of this paper is to assess the feasibility of automatic organ specific dose estimation. With respect to a commercially applicable solution and respective robustness and efficiency requirements, we investigated the effect of dose sampling rather than integration over the organ volume. We focused on the thoracic anatomy as the exemplary body region, imaged frequently by CT. For image segmentation we applied a set of available approaches which allowed us to cover the main thoracic radio-sensitive tissue types. We applied the dose estimation approach to 10 thoracic CT datasets and evaluated segmentation accuracy and administered dose and could show that organ specific dose estimation can be achieved.

Paper Details

Date Published: 21 March 2014
PDF: 15 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340A (21 March 2014); doi: 10.1117/12.2042998
Show Author Affiliations
Cristian Lorenz, Philips Research Labs. (Germany)
Heike Ruppertshofen, Philips Research Labs. (Germany)
Torbjörn Vik, Philips Research Labs. (Germany)
Peter Prinsen, Philips Research Labs. (Netherlands)
Jens Wiegert, Philips Research Labs. (Netherlands)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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