An analytical approach for the simulation of realistic low-dose fluoroscopic images

Hariharan SG, Strobel N, Kaethner C, Kowarschik M, Fahrig R, Navab N (2019)

Publication Type: Journal article

Publication year: 2019


Book Volume: 14

Pages Range: 601-610

Journal Issue: 4

DOI: 10.1007/s11548-019-01912-6


Purpose The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient. A possibility to address this issue is a realistic simulation of low-dose images from their related high-dose counterparts.MethodWe introduce a novel noise simulation method based on an X-ray image formation model. The method makes use of the system parameters associated with low- and high-dose X-ray image acquisitions, such as system gain and electronic noise, to preserve the image noise characteristics of low-dose images.ResultsWe have compared several corresponding regions of the associated real and simulated low-dose imagesobtained from two different imaging systemsvisually as well as statistically, using a two-sample Kolmogorov-Smirnov test at 5% significance. In addition to being visually similar, the hypothesis that the corresponding regionsfrom 80 pairs of real and simulated low-dose regionsbelonging to the same distribution has been accepted in 81.43% of the cases.ConclusionThe results suggest that the simulated low-dose images obtained using the proposed method are almost indistinguishable from real low-dose images. Since extensive calibration procedures required in previous methods can be avoided using the proposed approach, it allows an easy adaptation to different X-ray imaging systems. This in turn leads to an increased diversity of the training data for potential learning-based methods.

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Hariharan, S.G., Strobel, N., Kaethner, C., Kowarschik, M., Fahrig, R., & Navab, N. (2019). An analytical approach for the simulation of realistic low-dose fluoroscopic images. International Journal of Computer Assisted Radiology and Surgery, 14(4), 601-610.


Hariharan, Sai Gokul, et al. "An analytical approach for the simulation of realistic low-dose fluoroscopic images." International Journal of Computer Assisted Radiology and Surgery 14.4 (2019): 601-610.

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