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International Workshop on Machine Learning for Medical Image Reconstruction

MLMIR 2021: Machine Learning for Medical Image Reconstruction pp 109–119Cite as

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

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12964)

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.

Keywords

  • Fluoroscopic imaging
  • Video denoising
  • Real-time

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Author information

Authors and Affiliations

  1. Subtle Medical Inc., Menlo Park, CA, 94025, USA

    Dave Van Veen, Ben A. Duffy, Long Wang, Keshav Datta, Tao Zhang, Greg Zaharchuk & Enhao Gong

Authors
  1. Dave Van Veen
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  2. Ben A. Duffy
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  3. Long Wang
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  4. Keshav Datta
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  5. Tao Zhang
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  6. Greg Zaharchuk
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  7. Enhao Gong
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Corresponding author

Correspondence to Dave Van Veen .

Editor information

Editors and Affiliations

  1. Sunnybrook Health Science Centre, Toronto, ON, Canada

    Nandinee Haq

  2. NYU Grossman School of Medicine, New York City, NY, USA

    Patricia Johnson

  3. Pattern Recognition Lab at FAU, Erlangen, Germany

    Andreas Maier

  4. Siemens Healthineers, Erlangen, Germany

    Tobias Würfl

  5. Ulsan National Institute of Science and Technology, Ulsan, Korea (Republic of)

    Jaejun Yoo

Appendix

Appendix

Fig. 5.
figure 5

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.

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Table 3. Five-point Liekert scale used for reader study clinical evaluation.
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Table 4. Reader study scores. Positive mean scores denote our output is superior, which holds true for each category. We use this data to create a power analysis (95% confidence, 80% power) to estimate the number of paired low/normal dose samples required for a superiority test, n = 61 in the worst case. While clinical acquisition was beyond the scope of this preliminary work, we plan to collect this data in the future to pursue further clinical validation.
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Fig. 6.
figure 6

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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  • DOI: https://doi.org/10.1007/978-3-030-88552-6_11

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