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Proceedings Paper

Task-based image quality assessment in radiation therapy: initial characterization and demonstration with CT simulation images
Author(s): Steven R. Dolly; Mark A. Anastasio; Lifeng Yu; Hua Li
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Paper Abstract

In current radiation therapy practice, image quality is still assessed subjectively or by utilizing physically-based metrics. Recently, a methodology for objective task-based image quality (IQ) assessment in radiation therapy was proposed by Barrett et al.1 In this work, we present a comprehensive implementation and evaluation of this new IQ assessment methodology. A modular simulation framework was designed to perform an automated, computer-simulated end-to-end radiation therapy treatment. A fully simulated framework was created that utilizes new learning-based stochastic object models (SOM) to obtain known organ boundaries, generates a set of images directly from the numerical phantoms created with the SOM, and automates the image segmentation and treatment planning steps of a radiation therapy work ow. By use of this computational framework, therapeutic operating characteristic (TOC) curves can be computed and the area under the TOC curve (AUTOC) can be employed as a figure-of-merit to guide optimization of different components of the treatment planning process. The developed computational framework is employed to optimize X-ray CT pre-treatment imaging. We demonstrate that use of the radiation therapy-based-based IQ measures lead to different imaging parameters than obtained by use of physical-based measures.

Paper Details

Date Published: 10 March 2017
PDF: 6 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360Y (10 March 2017); doi: 10.1117/12.2254063
Show Author Affiliations
Steven R. Dolly, Washington Univ. in St. Louis (United States)
Mark A. Anastasio, Washington Univ. in St. Louis (United States)
Lifeng Yu, Mayo Clinic (United States)
Hua Li, Washington Univ. in St. Louis (United States)


Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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