Motion Artifact Recognition and Quantification in Coronary CT Angiography using Convolutional Neural Networks

Non-invasive coronary computed tomography angiography (CCTA) has become a preferred technique for the detection and diagnosis of coronary artery disease (CAD) (Budoff et  al., 2017; Foy et al., 2017; Camargo et al., 2017; Liu et al., 2017), but high quality imaging for small and moving vessels is still challenging. ECG-controlled acquisition is used to enable the reconstruction of heart phases with small motion level and gating windows are limited to the tempora l projection range required for back-projection.
Source: Medical Image Analysis - Category: Radiology Authors: Source Type: research