Real-time AI-enhanced Low Dose Fluoroscopy

Project Summary Motivation: Fluoroscopy is an indispensable tool for image-guided interventions in 50 million surgeries performed annually in the US. It leverages ionizing radiation from x-rays to provide surgeons with real-time, high-quality imaging feedback. Radiation exposure is harmful for both the patients and surgeons. Repetitive patient exposure has been shown to double the risk of breast cancer in women; meanwhile, surgeon exposure is also concerning. Orthopedic surgeons are 5x more likely to develop cancer in their lifetime; spine surgeons receive up to 12x more radiation exposure compared to other orthopedic surgeons. With minimally invasive surgery becoming widely adopted in recent years, the use of fluoroscopy has greatly increased. Surgeons rely on the navigation provided by fluoroscopy during these procedures, as they do not have direct visualization of the anatomy. Therefore, reducing the radiation exposure from fluoroscopy while maintaining high imaging quality is a high priority. In the past few years, artificial intelligence (AI) methods have shown promising advances to improve the quality of medical imaging. Subtle Medical Inc. has already received FDA clearance for its AI-based software products to reduce the dose for PET by four times and improve image quality for MRI. The motivation of this proposal is to translate our initial success to another imaging modality and achieve low dose fluoroscopy. Approach: This phase I SBIR project has three aims. Aim 1 is to develop AI software using recurrent deep learning architecture to achieve 6x dose reduction for fluoroscopy. Aim 2 is to design model pruning, kernel optimization, and high-performance inference frameworks to achieve real-time processing. Finally, in Aim 3, we will evaluate both qualitatively and quantitatively the developed software on phantoms and cadavers. Significance: This work will enable six times lower dose fluoroscopy. The completion of the project will have wide impact to greatly reduce radiation exposure in the operating room, hence reducing the risk of cancer development for both patients and clinicians.Project Narrative Fluoroscopy leverages ionizing radiation to provide surgeons with real-time, high-quality imaging feedback, indispensable for image-guided interventions in clinics. However, the radiation exposure introduces imaging risks, not only to the patients, but also to clinicians, who perform surgeries routinely. This project aims to develop and validate a fluoroscopy image enhancement software using artificial intelligence that can reduce the radiation dose by six folds.