Calcium removal from cardiac ct images using deep convolutional neural network

Siming Yan; Feng Shi; Yuhua Chen; Damini Dey; Sang Eun Lee; Hyuk Jae Chang; Debiao Li; Yibin Xie

doi:10.1109/ISBI.2018.8363617

Calcium removal from cardiac ct images using deep convolutional neural network

Siming Yan, Feng Shi, Yuhua Chen, Damini Dey, Sang Eun Lee, Hyuk Jae Chang, Debiao Li, Yibin Xie

Department of Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Coronary calcium causes beam hardening and blooming artifacts on cardiac computed tomography angiography (CTA) images, which lead to overestimation of lumen stenosis and reduction of diagnostic specificity. To properly remove coronary calcification and restore arterial lumen precisely, we propose a machine learning-based method with a multi-step inpainting process. We developed a new network configuration, Dense-Unet, to achieve optimal performance with low computational cost. Results after the calcium removal process were validated by comparing with gold-standard X-ray angiography. Our results demonstrated that removing coronary calcification from images with the proposed approach was feasible, and may potentially improve the diagnostic accuracy of CTA.

Original language	English
Title of host publication	2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
Publisher	IEEE Computer Society
Pages	466-469
Number of pages	4
ISBN (Electronic)	9781538636367
DOIs	https://doi.org/10.1109/ISBI.2018.8363617
Publication status	Published - 2018 May 23
Event	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States Duration: 2018 Apr 4 → 2018 Apr 7

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2018-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Other

Other	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country	United States
City	Washington
Period	18/4/4 → 18/4/7

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2018.8363617

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Yan, S., Shi, F., Chen, Y., Dey, D., Lee, S. E., Chang, H. J., Li, D., & Xie, Y. (2018). Calcium removal from cardiac ct images using deep convolutional neural network. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 466-469). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363617

Yan, Siming ; Shi, Feng ; Chen, Yuhua ; Dey, Damini ; Lee, Sang Eun ; Chang, Hyuk Jae ; Li, Debiao ; Xie, Yibin. / Calcium removal from cardiac ct images using deep convolutional neural network. 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. pp. 466-469 (Proceedings - International Symposium on Biomedical Imaging).

@inproceedings{30b7057da7f14b7ab0b9ba7cb924ad48,

title = "Calcium removal from cardiac ct images using deep convolutional neural network",

abstract = "Coronary calcium causes beam hardening and blooming artifacts on cardiac computed tomography angiography (CTA) images, which lead to overestimation of lumen stenosis and reduction of diagnostic specificity. To properly remove coronary calcification and restore arterial lumen precisely, we propose a machine learning-based method with a multi-step inpainting process. We developed a new network configuration, Dense-Unet, to achieve optimal performance with low computational cost. Results after the calcium removal process were validated by comparing with gold-standard X-ray angiography. Our results demonstrated that removing coronary calcification from images with the proposed approach was feasible, and may potentially improve the diagnostic accuracy of CTA.",

author = "Siming Yan and Feng Shi and Yuhua Chen and Damini Dey and Lee, {Sang Eun} and Chang, {Hyuk Jae} and Debiao Li and Yibin Xie",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",

year = "2018",

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doi = "10.1109/ISBI.2018.8363617",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

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Yan, S, Shi, F, Chen, Y, Dey, D, Lee, SE, Chang, HJ, Li, D & Xie, Y 2018, Calcium removal from cardiac ct images using deep convolutional neural network. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Proceedings - International Symposium on Biomedical Imaging, vol. 2018-April, IEEE Computer Society, pp. 466-469, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 18/4/4. https://doi.org/10.1109/ISBI.2018.8363617

Calcium removal from cardiac ct images using deep convolutional neural network. / Yan, Siming; Shi, Feng; Chen, Yuhua; Dey, Damini; Lee, Sang Eun; Chang, Hyuk Jae; Li, Debiao; Xie, Yibin.

2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. p. 466-469 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Coronary calcium causes beam hardening and blooming artifacts on cardiac computed tomography angiography (CTA) images, which lead to overestimation of lumen stenosis and reduction of diagnostic specificity. To properly remove coronary calcification and restore arterial lumen precisely, we propose a machine learning-based method with a multi-step inpainting process. We developed a new network configuration, Dense-Unet, to achieve optimal performance with low computational cost. Results after the calcium removal process were validated by comparing with gold-standard X-ray angiography. Our results demonstrated that removing coronary calcification from images with the proposed approach was feasible, and may potentially improve the diagnostic accuracy of CTA.

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