Automatic detection and tracking of the region of interest during fluoroscopy-guided procedures for radiation exposure reduction

Guy Nir; Lindsay S. Machan; Fergal Donnellan; Anna B. M. Brounstein; Kulwant Singh; D. Gordon Wait; Samuel V. Lichtenstein; Daniel Gelbart; Eran Elizur

doi:10.1117/12.2580729

15 February 2021 Automatic detection and tracking of the region of interest during fluoroscopy-guided procedures for radiation exposure reduction

Guy Nir, Lindsay S. Machan, Fergal Donnellan, Anna B. M. Brounstein, Kulwant Singh, D. Gordon Wait, Samuel V. Lichtenstein, Daniel Gelbart, Eran Elizur

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 1159828 (2021) https://doi.org/10.1117/12.2580729
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

The rise in number of fluoroscopy-guided procedures has prompted strategies for radiation management in order to minimize exposure to ionizing radiation in patients and healthcare personnel. The FluoroShield system utilizes an artificial intelligence (AI) approach towards radiation exposure reduction. The system automatically tracks the region of interest (ROI) in real-time, controls a rapid lead shutter for collimating the X-ray beam to the ROI, and blends the imaged ROI with the entire field of view that is updated at a lower frame rate to present, at all times, a full image to the operator. In this work, we discuss the AI-based component of the system that is used for tracking the ROI in endoscopic procedures. Known as the auto-ROI processor (ARP), the methodology comprises a merged architecture of convolutional neural networks for object detection that allows for both fine and contextual features to be captured. The detection probabilities are incorporated within a particle filtering framework for spatial and temporal updating of the ROI in a Bayesian approach. The ARP's performance is evaluated on fluoroscopic sequences, taking into consideration the estimated radiation reduction rates. The reduction in radiation exposure based on simulations is comparable to the reported values in a clinical study.

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Guy Nir, Lindsay S. Machan, Fergal Donnellan, Anna B. M. Brounstein, Kulwant Singh, D. Gordon Wait, Samuel V. Lichtenstein, Daniel Gelbart, and Eran Elizur "Automatic detection and tracking of the region of interest during fluoroscopy-guided procedures for radiation exposure reduction", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 1159828 (15 February 2021); https://doi.org/10.1117/12.2580729

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