In current radiation therapy (RT) practice, image quality is still assessed subjectively or by utilizing physically-based metrics. Recently, a novel theory for objective task-based image quality assessment (IQA) in RT was proposed by use of area under therapeutic operating characteristic curve (AUTOC) as the figure-of-merit (FOM).
In this study, we present a comprehensive and practical modular IQA-in-RT simulation framework of this novel theory and evaluate its performance with case studies. The IQA-in-RT simulation framework is created that utilizes new learning-based stochastic object models (SOM) to obtain known organ boundaries, generates ensemble of images directly from the numerical phantoms created with the SOM, and automates the image segmentation and treatment planning steps of a common radiation therapy workflow. By use of this simulation framework, therapeutic operating characteristic (TOC) curves can be computed and the AUTOC can be employed as a FOM to guide optimization of different components of RT process.
The AUTOC compared to various IQA metrics on the two applications of optimizing imaging dose and image reconstructions.
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