|Author||Dolly, S. ♦ Mutic, S. ♦ Anastasio, M. ♦ Li, H. ♦ Yu, L.|
|Source||United States Department of Energy Office of Scientific and Technical Information|
|Subject Keyword||APPLIED LIFE SCIENCES ♦ RADIATION PROTECTION AND DOSIMETRY ♦ ALGORITHMS ♦ BIOMEDICAL RADIOGRAPHY ♦ COMPUTERIZED SIMULATION ♦ COMPUTERIZED TOMOGRAPHY ♦ EXTERNAL BEAM RADIATION THERAPY ♦ FLEXIBILITY ♦ IMAGE PROCESSING ♦ IMAGES ♦ OPTIMIZATION ♦ PATIENTS ♦ PHANTOMS ♦ RADIATION DOSES|
|Abstract||Purpose: Traditionally, image quality in radiation therapy is assessed subjectively or by utilizing physically-based metrics. Some model observers exist for task-based medical image quality assessment, but almost exclusively for diagnostic imaging tasks. As opposed to disease diagnosis, the task for image observers in radiation therapy is to utilize the available images to design and deliver a radiation dose which maximizes patient disease control while minimizing normal tissue damage. The purpose of this study was to design and implement a new computer simulation model observer to enable task-based image quality assessment in radiation therapy. Methods: A modular computer simulation framework was developed to resemble the radiotherapy observer by simulating an end-to-end radiation therapy treatment. Given images and the ground-truth organ boundaries from a numerical phantom as inputs, the framework simulates an external beam radiation therapy treatment and quantifies patient treatment outcomes using the previously defined therapeutic operating characteristic (TOC) curve. As a preliminary demonstration, TOC curves were calculated for various CT acquisition and reconstruction parameters, with the goal of assessing and optimizing simulation CT image quality for radiation therapy. Sources of randomness and bias within the system were analyzed. Results: The relationship between CT imaging dose and patient treatment outcome was objectively quantified in terms of a singular value, the area under the TOC (AUTOC) curve. The AUTOC decreases more rapidly for low-dose imaging protocols. AUTOC variation introduced by the dose optimization algorithm was approximately 0.02%, at the 95% confidence interval. Conclusion: A model observer has been developed and implemented to assess image quality based on radiation therapy treatment efficacy. It enables objective determination of appropriate imaging parameter values (e.g. imaging dose). Framework flexibility allows for incorporation of additional modules to include any aspect of the treatment process, and therefore has great potential for both assessment and optimization within radiation therapy.|
|Learning Resource Type||Article|
|Publisher Place||United States|
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