Calcium Removal From Cardiac CT Images Using Deep Convolutional Neural Network

TL;DR

This paper introduces Dense-Unet, a deep learning model for removing coronary calcium from cardiac CT images, aiming to improve diagnostic accuracy by reducing artifacts.

Contribution

The study presents a novel Dense-Unet architecture for calcium removal in cardiac CT images, combining multi-step inpainting with low computational cost.

Findings

Feasibility of calcium removal demonstrated

Improved image quality after processing

Potential enhancement of diagnostic accuracy

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.