Abstract
Purpose
2D digital subtraction angiography (DSA) has become an important technique for interventional neuroradiology tasks, such as detection and subsequent treatment of aneurysms. In order to provide high-quality DSA images, usually undiluted contrast agent and a high X-ray dose are used. The iodinated contrast agent puts a burden on the patients’ kidneys while the use of high-dose X-rays expose both patients and medical staff to a considerable amount of radiation. Unfortunately, reducing either the X-ray dose or the contrast agent concentration usually results in a sacrifice of image quality.
Materials and methods
To denoise a frame, the proposed spatiotemporal denoising method utilizes the low-rank nature of a spatially aligned temporal sequence where variation is introduced by the flow of contrast agent through a vessel tree of interest. That is, a constrained weighted rank-1 approximation of the stack comprising the frame to be denoised and its temporal neighbors is computed where the weights are used to prevent the contribution of non-similar pixels toward the low-rank approximation. The method has been evaluated using a vascular flow phantom emulating cranial arteries into which contrast agent can be manually injected (Vascular Simulations Replicator, Vascular Simulations, Stony Brook NY, USA). For the evaluation, image sequences acquired at different dose levels as well as different contrast agent concentrations have been used.
Results
Qualitative and quantitative analyses have shown that with the proposed approach, the dose and the concentration of the contrast agent could both be reduced by about 75%, while maintaining the required image quality. Most importantly, it has been observed that the DSA images obtained using the proposed method have the closest resemblance to typical DSA images, i.e., they preserve the typical image characteristics best.
Conclusion
Using the proposed denoising approach, it is possible to improve the image quality of low-dose DSA images. This improvement could enable both a reduction in contrast agent and radiation dose when acquiring DSA images, thereby benefiting patients as well as clinicians. Since the resulting images are free from artifacts and as the inherent characteristics of the images are also preserved, the proposed method seems to be well suited for clinical images as well.
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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
Bogunović H, Lončarić S (2005) Denoising of time-density data in digital subtraction angiography. Springer, Berlin, pp 1157–1166
Buades A, Coll B, Morel JM (2008) Nonlocal image and movie denoising. Int J Comput Vision 76(2):123–139. https://doi.org/10.1007/s11263-007-0052-1
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
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
Dasa S, Neumaiera A (2011) Fast regularized low rank approximation of weighted data sets. www.mat.univie.ac.at/%7Eneum/software/birsvd/svd%5Fincomplete%5Fdata.pdf
Deng X, Liu Z (2015) Image denoising based on steepest descent OMP and K-SVD. In: 2015 IEEE international conference on signal processing, communications and computing (ICSPCC), pp 1–5 (2015). https://doi.org/10.1109/ICSPCC.2015.7338821
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 Comput Assist Radiol Surg 13(6):847–854
Hariharan SG, Strobel N, Kaethner C, Kowarschik M, Fahrig R, Navab N (2019) An analytical approach for the simulation of realistic low-dose fluoroscopic images. Int J Comput Assist Radiol Surg 14(4):1–10
Kostadin D, Alessandro F, Karen E (2007) Video denoising by sparse 3D transform-domain collaborative filtering. In: European signal processing conference, vol 149. Tampere, Finland
Lebrun M (2012) An analysis and implementation of the BM3D image denoising method. Image Process Online 2:175–213. https://doi.org/10.5201/ipol.2012.l-bm3d
Lebrun M, Leclaire A (2012) An implementation and detailed analysis of the K-SVD image denoising algorithm. Image Process Online 2:96–133. https://doi.org/10.5201/ipol.2012.llm-ksvd
Luisier F, Blu T, Unser M (2010) SURE-LET for orthonormal wavelet-domain video denoising. IEEE Trans Circuits Syst Video Technol 20(6):913–919. https://doi.org/10.1109/TCSVT.2010.2045819
Luisier F, Blu T, Unser M (2011) Image denoising in mixed Poisson–Gaussian noise. IEEE Trans Image Process 20(3):696–708
Luisier F, Vonesch C, Blu T, Unser M (2010) Fast interscale wavelet denoising of Poisson-corrupted images. Signal Process 90(2):415–427
Makitalo M, Foi A (2013) Optimal inversion of the generalized Anscombe transformation for Poisson–Gaussian noise. IEEE Trans Image Process 22(1):91–103
Markovsky I, Niranjan M (2010) Approximate low-rank factorization with structured factors. Comput Stat Data Anal 54(12):3411–3420
Menger N, Elsässer T, Chen GH, Manhart M (2017) Noise reduction in low dose DSA imaging using pixel adaptive SVD-based approach. In: Maier-Hein KH, geb. Fritzsche, Deserno TM, geb. Lehmann, Handels H, Tolxdorff T (eds) Bildverarbeitung für die Medizin 2017. Springer, Berlin, pp 37–42
Niu K, Li Y, Schafer S, Royalty K, Wu Y, Strother C, Chen GH (2015) Ultra low radiation dose digital subtraction angiography (DSA) imaging using low rank constraint. In: SPIE medical imaging. International Society for Optics and Photonics, p 941210
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Söderman M, Holmin S, Andersson T, Palmgren C, Babić D, Hoornaert B (2013) Image noise reduction algorithm for digital subtraction angiography: clinical results. Radiology 269(2):553–560
Srebro N, Jaakkola T (2003) Weighted low-rank approximations. In: Proceedings of the 20th international conference on machine learning (ICML-03), pp 720–727
Starck JL, Murtagh FD, Bijaoui A (1998) Image processing and data analysis: the multiscale approach. Cambridge University Press, Cambridge
Weickert J, Scharr H (2002) A scheme for coherence-enhancing diffusion filtering with optimized rotation invariance. J Vis Commun Image Represent 13(1–2):103–118
Wolterink JM, Leiner T, Viergever MA, Išgum I (2017) Generative adversarial networks for noise reduction in low-dose ct. IEEE Trans Med Imaging 36(12):2536–2545
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(7):3142–3155
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This work was supported by Siemens Healthineers AG.
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Hariharan, S.G., Kaethner, C., Strobel, N. et al. Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system. Int J CARS 14, 1117–1126 (2019). https://doi.org/10.1007/s11548-019-01968-4
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DOI: https://doi.org/10.1007/s11548-019-01968-4