A photon recycling approach to the denoising of ultra-low dose X-ray sequences

  • Sai Gokul Hariharan
  • Norbert Strobel
  • Christian Kaethner
  • Markus Kowarschik
  • Stefanie Demirci
  • Shadi Albarqouni
  • Rebecca Fahrig
  • Nassir Navab
Original Article



Clinical procedures that make use of fluoroscopy may expose patients as well as the clinical staff (throughout their career) to non-negligible doses of radiation. The potential consequences of such exposures fall under two categories, namely stochastic (mostly cancer) and deterministic risks (skin injury). According to the “as low as reasonably achievable” principle, the radiation dose can be lowered only if the necessary image quality can be maintained.


Our work improves upon the existing patch-based denoising algorithms by utilizing a more sophisticated noise model to exploit non-local self-similarity better and this in turn improves the performance of low-rank approximation. The novelty of the proposed approach lies in its properly designed and parameterized noise model and the elimination of initial estimates. This reduces the computational cost significantly.


The algorithm has been evaluated on 500 clinical images (7 patients, 20 sequences, 3 clinical sites), taken at ultra-low dose levels, i.e. 50% of the standard low dose level, during electrophysiology procedures. An average improvement in the contrast-to-noise ratio (CNR) by a factor of around 3.5 has been found. This is associated with an image quality achieved at around 12 (square of 3.5) times the ultra-low dose level. Qualitative evaluation by X-ray image quality experts suggests that the method produces denoised images that comply with the required image quality criteria.


The results are consistent with the number of patches used, and they demonstrate that it is possible to use motion estimation techniques and “recycle” photons from previous frames to improve the image quality of the current frame. Our results are comparable in terms of CNR to Video Block Matching 3D—a state-of-the-art denoising method. But qualitative analysis by experts confirms that the denoised ultra-low dose X-ray images obtained using our method are more realistic with respect to appearance.


Spatio-temporal denoising Ultra-low dose X-ray sequences Low-rank approximation 



This work was supported by Siemens Healthcare GmbH. The concepts and results presented in this paper are based on research and not commercially available.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.


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Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
  2. 2.Siemens Healthcare GmbH, Advanced TherapiesForchheimGermany
  3. 3.Fakultät für ElektrotechnikHochschule für angewandte Wissenschaften Würzburg-SchweinfurtSchweinfurtGermany
  4. 4.Whiting School of EngineeringJohns Hopkins UniversityBaltimoreUSA
  5. 5.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany

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