@inproceedings{046db0fad0f24d238aeddba94a697224,
title = "Sparsity-based sinogram denoising for low-dose computed tomography",
abstract = "We propose a sinogram restoration method which consists of a patch-wise non-linear processing, based on a sparsity prior in terms of a learned dictionary. An off-line learning process uses a statistical model of the sinogram noise and minimizes an error measure in the image domain over the training set. The error measure is designed to preserve low-contrast edges for visibility of soft tissues. Our numerical study shows that the algorithm improves on the performance of the standard Filtered Back-Projection algorithm and effectively allows to halve the radiation dose for the same image quality.",
keywords = "Computed Tomography, Sparse-Land paradigm, sinogram restoration",
author = "J. Shtok and M. Elad and M. Zibulevsky",
note = "Funding Information: National Natural Science Foundation of China 81872011 81572392 Funding Information: This study was supported, in part, by the National Key R&D Program of China (2018YFC1313300), National Key Research and Development Program of China (2017YFC1308900), Natural Science Foundation of Guangdong Province (2014A030312015, 2017A030313485); National Natural Science Foundation of China (81872011, 81572392), Science and Technology Program of Guangdong (2019B020227002) and the Sun Yat-sen University Clinical Research 5010 Program (2018014). FW is the Young Physician Scientist Program of Sun Yat-sen University Cancer. Funding Information: This study is sponsored by Shanghai Junshi Biosciences. We gratefully thank the patients and their families for participating in this study. This study was supported, in part, by the National Key R&D Program of China (2018YFC1313300), National Key Research and Development Program of China (2017YFC1308900), Natural Science Foundation of Guangdong Province (2014A030312015, 2017A030313485); National Natural Science Foundation of China (81872011, 81572392), Science and Technology Program of Guangdong (2019B020227002) and the Sun Yat-sen University Clinical Research 5010 Program (2018014). FW is the Young Physician Scientist Program of Sun Yat-sen University Cancer. National Key R&D Program of China2018YFC1313300, National Key Research and Development Program of China2017YFC1308900, Natural Science Foundation of Guangdong Province2014A0303120152017A030313485, National Natural Science Foundation of China8187201181572392, Science and Technology Program of Guangdong2019B020227002, Sun Yat-sen University Clinical Research 5010 Program2018014, Young Physician Scientist Program of Sun Yat-sen University Cancer, HW, HF, and SY declare employment with Shanghai Junshi Biosciences Co. Ltd. All remaining authors have declared no conflicts of interest. Funding Information: Natural Science Foundation of Guangdong Province 2014A030312015 2017A030313485 Funding Information: This study is sponsored by Shanghai Junshi Biosciences. We gratefully thank the patients and their families for participating in this study.; 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 ; Conference date: 22-05-2011 Through 27-05-2011",
year = "2011",
doi = "10.1109/ICASSP.2011.5946467",
language = "الإنجليزيّة",
isbn = "9781457705397",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "569--572",
booktitle = "2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings",
}
