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.
|Title of host publication||2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|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
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Other||15th IEEE International Symposium on Biomedical Imaging, ISBI 2018|
|Period||18/4/4 → 18/4/7|
Bibliographical notePublisher Copyright:
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging