Abstract
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.
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Appendix
Appendix
Training pair simulation using the method outlined in Fig. 1, which inputs the normal dose video and outputs both a ground truth and a low dose video. We note the noise map is free of structure and artifacts.
Single-frame results from a fluoroscopy video. Metrics are calculated with respect to ground truth.
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Van Veen, D. et al. (2021). Real-Time Video Denoising to Reduce Ionizing Radiation Exposure in Fluoroscopic Imaging. In: Haq, N., Johnson, P., Maier, A., Würfl, T., Yoo, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2021. Lecture Notes in Computer Science(), vol 12964. Springer, Cham. https://doi.org/10.1007/978-3-030-88552-6_11
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