CALCIUM REMOVAL FROM CARDIAC CT IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK

被引：0
|
作者：
Yan, Siming [1 ,2 ]
Shi, Feng [1 ]
Chen, Yuhua [1 ,3 ]
Dey, Damini [1 ]
Lee, Sang-Eun [1 ,4 ]
Chang, Hyuk-Jae [4 ]
Li, Debiao [1 ,3 ]
Xie, Yibin [1 ]
机构：
[1] Cedars Sinai Med Ctr, Biomed Imaging Res Inst, Los Angeles, CA 90048 USA
[2] Peking Univ, Dept Comp Sci, Beijing, Peoples R China
[3] Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA USA
[4] Yonsei Univ, Coll Med, Div Cardiol, Seoul, South Korea
来源：
2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018) | 2018年
关键词：
Coronary calcium; Deep neural network; Cardiac CT angiography;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
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.
引用
收藏
页码：466 / 469
页数：4
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