  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.
