AI-based collimation optimization for X-ray imaging using depth cameras

Abstract

Collimation during radiography, which is the process of defining the area to be radiated, is a crucial factor for the protection of the patient and for the diagnostic quality of a radiograph. Moreover, incorrect collimation is one of the main causes of a retake and the associated costs. In this paper we propose a novel collimation optimization approach using depth cameras and deep neural networks trained end-to-end. We have acquired two new datasets for this purpose. The first, obtained in a clinical environment, consists of depth images of the lower leg and abdomen and the second, captured in real clinical practice, consists of depth images and corresponding radiographs of thorax examinations. For all depth images, the ideal collimation was labeled by experts either on the depth image or directly on the radiograph. Using this dataset to learn to predict the optimal collimation, we show that it is possible to learn different shapes of collimations and to achieve results that are on par with those obtained by radiographers. Such an AI assistant trained with optimal collimation could reduce the radiation exposure to which the patient is exposed, improve the workflow in radiography, and finally increase the diagnostic quality of radiographs.