Tunable and real-time automatic interventional x-ray collimation from semi-supervised deep feature extraction

BackgroundThe use of endovascular procedures is becoming increasingly popular across multiple clinical domains. These procedures are generally performed under image guidance using an interventional c-arm x-ray system. Radiation exposure to both patients and interventional staff due to use of fluoroscopy is a health and occupational concern, but modifications to the interventional workflow to address radiation may come at the cost of procedure time or quality.PurposeInterventional x-ray collimation is a crucial task for improving image quality as well as reducing radiation exposure to both patients and operators who work in the x-ray domain. However, collimation is heavily underutilized due to its cumbersome nature and the difficulty of manually manipulating multiple parameters during fast-paced interventional procedures. Additionally, the widely varying collimation preferences of interventionalists across different procedure types, procedure phases, and anatomies makes the standardizing of collimation challenging for radiation technologist support staff.MethodsAutomating collimation has the potential to bridge this gap, freeing up mental bandwidth for interventionalists and technologists and improving outcomes for patients. Here, we propose a tunable algorithm for automatic collimation based on a region-of-interest optimizer driven by a combination of image, system, device, and radiation based features and we illustrate its efficacy across varying personal preferences. Critically, we devise a method with a simple and easily understandable mapping between algorithm parameters and practical outcomes.ResultsWe show a real-time implementation of this algorithm using deep feature extraction by a convolutional neural network and evaluate its performance in a custom dataset of simulated fluoroscopy and recorded fluoroscopy from clinical radial access procedures. We evaluate the effects of a practically implemented mixed supervision training strategy on model performance and show potential for radiation reduction in simulation. An uncertainty analysis indicates that the algorithm is robust to noise and anatomical variation across our clinical dataset. Clinical acceptability and quality is evaluated through a reader study with expert neuro-interventional radiologists, with participants indicating 100% clinical acceptability, high quality ratings, and improved radiation protection over their typical practice.ConclusionsThe algorithm's modular design ensured that users' collimation requirements were met without disruption to the interventional workflow or procedure time, while exhibiting strong potential to reduce radiation risk to patients and operators. Evaluation in more varied clinical settings could support translation of this technology into the clinic.