Patient pose assessment in radiography using time-of-flight cameras

The correct pose of the patient during radiography is of critical importance to ensure an adequate diagnostic quality of radiographs, which are the basis for diagnosis and treatment planning. However, correct patient positioning is not a standardized process, often resulting in inadequate radiographs and repeated radiation exposure. We propose a novel approach using Time-of-Flight cameras to assess the patient’s pose and therefore predict the expected diagnostic quality of the radiograph, before it is even captured. As a first step towards this goal, we acquired a new dataset, consisting of depth images and corresponding radiographs of the ankle using two anatomical preparations in multiple poses. The radiographs were labeled by radiologists for their diagnostic quality related to the patient’s pose. These labels serve as quality label for the corresponding pose. Using this dataset we trained deep neural networks and were able to correctly assess the diagnostic quality of a pose with a mean accuracy of up to 90.2%, demonstrating that shared features for pose assessment across patients exist and can be learned.