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Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.
IEEE Trans Med Imaging 2018; 37(6):1358-1369IT

Abstract

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.

Authors

No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

29870365

Citation

Kang, Eunhee, et al. "Deep Convolutional Framelet Denosing for Low-Dose CT Via Wavelet Residual Network." IEEE Transactions On Medical Imaging, vol. 37, no. 6, 2018, pp. 1358-1369.
Kang E, Chang W, Yoo J, et al. Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network. IEEE Trans Med Imaging. 2018;37(6):1358-1369.
Kang, E., Chang, W., Yoo, J., & Ye, J. C. (2018). Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network. IEEE Transactions On Medical Imaging, 37(6), pp. 1358-1369. doi:10.1109/TMI.2018.2823756.
Kang E, et al. Deep Convolutional Framelet Denosing for Low-Dose CT Via Wavelet Residual Network. IEEE Trans Med Imaging. 2018;37(6):1358-1369. PubMed PMID: 29870365.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network. AU - Kang,Eunhee, AU - Chang,Won, AU - Yoo,Jaejun, AU - Ye,Jong Chul, PY - 2018/6/6/entrez PY - 2018/6/6/pubmed PY - 2019/5/21/medline SP - 1358 EP - 1369 JF - IEEE transactions on medical imaging JO - IEEE Trans Med Imaging VL - 37 IS - 6 N2 - Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images. SN - 1558-254X UR - https://www.unboundmedicine.com/medline/citation/29870365/Deep_Convolutional_Framelet_Denosing_for_Low_Dose_CT_via_Wavelet_Residual_Network_ L2 - https://dx.doi.org/10.1109/TMI.2018.2823756 DB - PRIME DP - Unbound Medicine ER -