Abstract
In the last decade, the technological progress of multi-slice CT imaging has turned CCTA into a valuable tool for coronary assessment in many low to medium risk patients. Nevertheless, CCTA protocols expose the patient to high radiation doses, imposed by image quality and multiple cardiac phase acquisition requirements. Widespread use of CCTA calls for significant reduction of radiation exposure while maintaining high image quality as required for coronary assessment. Denoising algorithms have been recently applied to low-dose CT scans after image reconstruction. In this work, a fast neural regression framework is proposed for the denoising of low-dose CCTA. For this purpose, regression networks are trained to synthesize high-SNR patches directly from low-SNR input patches. In contrast to published methods, the denoising network is trained on real noise directly learned from noisy CT data rather than assuming a known parametric noise model. The denoised value for each pixel is computed as a function of the synthesized patches overlapping the pixel. The proposed algorithm is compared to state-of-the-art published algorithms for synthetic and real noise. The feature similarity index (FSIM) achieved by the proposed method is superior in all the comparisons with other methods, for synthetic radiation dose reductions higher than 90%. The results are further supported qualitatively, by observing a significant improvement in subsequent coronary reconstruction performed by commercial software on denoised images. The fast and high quality denoising capability suggests the proposed algorithm as a promising method for low-dose CCTA denoising.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Panetta, D.: Advances in X-ray detectors for clinical and preclinical computed tomography. Nucl. Instrum. Methods Phys. Res. Sect. A: Accel. Spectrom. Detect. Assoc. Equip. 809, 2–12 (2016)
Stefanini, G.G., Windecker, S.: Can coronary computed tomography angiography replace invasive angiography? Circulation 131(4), 418–426 (2015)
Sun, Z., Sabarudin, A.: Coronary CT angiography: state of the art. World J. Cardiol. 5(12), 442 (2013)
Joemai, R.M., Geleijns, J., Veldkamp, W.J., de Roos, A., Kroft, L.J.: Automated cardiac phase selection with 64-MDCT coronary angiography. Am. J. Roentgenol. 191(6), 1690–1697 (2008)
Kang, D., Slomka, P., Nakazato, R., Woo, J., Berman, D.S., Kuo, C.-C.J., Dey, D.: Image denoising of low-radiation dose coronary CT angiography by an adaptive block-matching 3D algorithm. In: SPIE Medical Imaging. International Society for Optics and Photonics (2013)
Green, M., Marom, E.M., Kiryati, N., Konen, E., Mayer, A.: Efficient low-dose CT denoising by locally-consistent non-local means (LC-NLM). In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 423–431. Springer, Cham (2016). doi:10.1007/978-3-319-46726-9_49
Chen, H., Zhang, Y., Zhang, W., Liao, P., Li, K., Zhou, J., Wang, G.: Low-dose CT via convolutional neural network. Biomed. Opt. Express 8(2), 679–694 (2017)
Chen, F., Zhang, L., Yu, H.: External patch prior guided internal clustering for image denoising. In: IEEE ICCV (2015)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
McNitt-Gray, M.F.: AAPM/RSNA physics tutorial for residents: topics in CT: radiation dose in CT 1. Radiographics 22(6), 1541–1553 (2002)
Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Green, M., Marom, E.M., Kiryati, N., Konen, E., Mayer, A. (2017). A Neural Regression Framework for Low-Dose Coronary CT Angiography (CCTA) Denoising. In: Wu, G., Munsell, B., Zhan, Y., Bai, W., Sanroma, G., Coupé, P. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2017. Lecture Notes in Computer Science(), vol 10530. Springer, Cham. https://doi.org/10.1007/978-3-319-67434-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-67434-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67433-9
Online ISBN: 978-3-319-67434-6
eBook Packages: Computer ScienceComputer Science (R0)