Abstract
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.
Method
We 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.
Results
We have compared several corresponding regions of the associated real and simulated low-dose images—obtained from two different imaging systems—visually 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 regions—from 80 pairs of real and simulated low-dose regions—belonging to the same distribution has been accepted in 81.43% of the cases.
Conclusion
The 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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References
Amiot C, Girard C, Chanussot J, Pescatore J, Desvignes M (2016) Spatio-temporal multiscale denoising of fluoroscopic sequence. IEEE Trans Med Imaging 35(6):1565–1574
Cerciello T, Bifulco P, Cesarelli M, Fratini A (2012) A comparison of denoising methods for X-ray fluoroscopic images. Biomed Signal Process Control 7(6):550–559
Chan CL, Sullivan BJ, Sahakian AV, Katsaggelos AK, Swiryn S, Hueter DC, Frohlich T (1990) Simulation of quantum mottle in digital angiographic images. Biomed Image Process 1245:104–111 SPIE
Chen Y, Pock T (2017) Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans Pattern Anal Mach Intell 39(6):1256–1272
Dabov K, Foi A, Katkovnik V, Egiazarian K (2009) BM3D image denoising with shape-adaptive principal component analysis. In: Signal processing with adaptive sparse structured representations
Gislason-Lee AJ, Kumcu A, Kengyelics SM, Brettle DS, Treadgold LA, Sivananthan M, Davies AG (2015) How much image noise can be added in cardiac x-ray imaging without loss in perceived image quality? J Electron Imaging 24(5):051006
Hariharan SG, Kaethner C, Strobel N, Kowarschik M, Fahrig R, Navab N (2018) Estimation of noise stabilization parameters for low-dose X-ray images corrupted by Poisson–Gaussian noise. (manuscript in preparation)
Hariharan SG, Strobel N, Kaethner C, Kowarschik M, Demirci S, Albarqouni S, Fahrig R, Navab N (2018) A photon recycling approach to the denoising of ultra-low dose X-ray sequences. Int J CARS 13:847–854. https://doi.org/10.1007/s11548-018-1746-2
Hariharan SG, Strobel N, Kowarschik M, Fahrig R, Navab N (2018) Simulation of realistic low dose fluoroscopic images from their high dose counterparts. In: Bildverarbeitung für die Medizin 2018, Springer, pp 80–85
Holdsworth D, Gerson R, Fenster A (1990) A time-delay integration charge-coupled device camera for slot-scanned digital radiography. Med Phys 17(5):876–886
Kostadin D, Alessandro F, Karen E (2007) Video denoising by sparse 3D transform-domain collaborative filtering. In: European signal processing conference, vol 149
Kuan DT, Sawchuk AA, Strand TC, Chavel P (1985) Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans Pattern Anal Mach Intell 2:165–177
Luisier F, Blu T, Unser M (2007) A new sure approach to image denoising: interscale orthonormal wavelet thresholding. IEEE Trans Image Process 16(3):593–606
Makitalo M, Foi A (2013) Optimal inversion of the generalized anscombe transformation for poisson-gaussian noise. IEEE Trans Image Process 22(1):91–103
Massey FJ Jr (1951) The Kolmogorov–Smirnov test for goodness of fit. J Am Stat Assoc 46(253):68–78
Matviychuk Y, Mailhé B, Chen X, Wang Q, Kiraly A, Strobel N, Nadar M (2016) Learning a multiscale patch-based representation for image denoising in X-ray fluoroscopy. In: Proceedings of international conference on image processing, pp 2330–2334
Schmidt U, Roth S (2014) Shrinkage fields for effective image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2774–2781
Starck JL, Murtagh FD, Bijaoui A (1998) Image processing and data analysis: the multiscale approach. Cambridge University Press, Cambridge
Veldkamp WJ, Kroft LJ, van Delft JPA, Geleijns J (2009) A technique for simulating the effect of dose reduction on image quality in digital chest radiography. J Digit Imaging 22(2):114–125
Wolterink JM, Leiner T, Viergever MA, Isgum I (2017) Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans Med Imaging 36:2536–2545
Yang K, Huang SY, Packard NJ, Boone JM (2010) Noise variance analysis using a flat panel X-ray detector: a method for additive noise assessment with application to breast ct applications. Med Phys 37(7):3527–3537
Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a Gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans Image Process 26:3142–3155
Acknowledgements
This work was supported by Siemens Healthineers AG. The concepts and results presented in this paper are based on research and are not commercially available.
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Hariharan, S.G., Strobel, N., Kaethner, C. et al. An analytical approach for the simulation of realistic low-dose fluoroscopic images. Int J CARS 14, 601–610 (2019). https://doi.org/10.1007/s11548-019-01912-6
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DOI: https://doi.org/10.1007/s11548-019-01912-6