Real-Time Video Denoising to Reduce Ionizing Radiation Exposure in Fluoroscopic Imaging

Fluoroscopic imaging relies on ionizing radiation to provide physicians with high quality video feedback during a surgical operation. Radiation exposure is harmful for both the physician and patient, but reducing dosage results in a much noisier video. We hence propose an algorithm that delivers the same quality video with \(4{\times }\) reduction in radiation dose. Our method is a deep learning approximation to VBM4D, a state-of-the-art video denoiser. Neither VBM4D nor previous deep learning methods are clinically feasible, however, as their high inference runtimes prohibit live display on an operating room monitor. On the other hand, we present a video denoising method which executes orders of magnitude faster while achieving state-of-the-art performance. This provides compelling potential for real-time clinical application in fluoroscopic imaging.