An intelligent X-ray assistant

Abstract

Radiographs are the most commonly used modality in diagnostic imaging. Although they are of fundamental importance, there are constantly images acquired that cannot be used for diagnosis. Since the development of digital radiography, such unusable images have mainly been caused by incorrect patient positioning or improper collimation. Importantly, these errors can directly impact patients' health. Repeating the procedure increases radiation exposure, delays treatment, and extends hospital stays. Poor-quality images can even lead to misdiagnosis and incorrect treatment if unnoticed. Ultimately, images that cannot be used for diagnostic purposes also harm the hospital by increasing costs, staff workload, and room occupancy.

To improve radiograph image quality, this thesis develops deep learning based methods for assistance systems in the radiography process. First, a developed framework is used to learn how to automatically assess the quality of radiographs. While radiologists can usually assess quality immediately, radiographers often come to different judgments. The automatic assessment system can provide support here and spare patients unnecessary repeat examinations. The results show that deep learning models are capable of assessing quality at the level of a radiologist across different parts of the body.

Based on these results, instead of assessing radiographs, the quality of radiographs is predicted using depth images. While automatic assessment allows low-quality images to be detected immediately, it cannot directly prevent them from being produced. To achieve this, depth cameras were used to record the patient's pose, and neural networks were then used to predict the quality of the resulting radiograph. This type of positioning assessment makes it possible to acquire a radiograph only when a poor-quality image is unlikely to result. Although quality can only be determined on the radiographs themselves, the results show that a similarly accurate prediction is possible on depth images.

In addition to positioning, the irradiation area is another key factor affecting radiograph quality. Therefore, a system is developed to automatically determine the required irradiation area. Depth cameras are used to capture images of the patients, while simultaneously acquired radiographs are used to label the minimally necessary irradiation area. The system learns to predict the required area automatically and demonstrates performance comparable to that of trained radiographers.

Overall, this thesis presents approaches to solving the most common causes of low-quality radiographs today. It presents the first studies to systematically evaluate radiographs or patient poses in terms of their suitability for diagnosis.