Dr. Ranish Khawaja, along with Dr. Gary Schooler and Dr. Ehsan Samei from Duke Radiology recently presented during RSNA meeting 2017 about their development of an automated image quality assessment tool for pediatric radiographs based on 10 perceptual attributes of adult chest radiographs, such as lung gray levels, lung details, rib-lung contrast, and mediastinum. Traditionally, image quality has relied on just a few physical measures such as noise and contrast, while observer studies are highly subjective by nature. This adaptation improved the performance of the algorithm, which now can be used for a variety of applications beyond image quality assessment, including protocol development, quality monitoring, and integration with dose estimates to improve benchmarks for pediatric chest radiography. The group also plans to explore whether machine learning could be used for the application.
