# Calcium Removal From Cardiac CT Images Using Deep Convolutional Neural Network

arXiv: https://arxiv.org/abs/1803.00399 · HTML: https://mlsanity.com/paper/1803.00399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [Self-Supervised Masked Autoencoders with Dense-Unet for Coronary Calcium Removal in limited CT Data](https://mlsanity.com/paper/2601.02392/) (2025)

## Abstract (arXiv)

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.