Deep-learning-based CT motion artifact recognition in coronary arteries

First published in
Progress in Biomedical Optics and Imaging - Proceedings of SPIE  
Number in series
10574
Article Number
1057416
Citation
Progress in Biomedical Optics and Imaging - Proceedings of SPIE (10574): 1057416 (2018)
Contribution to Conference
Progress in Biomedical Optics and Imaging (SPIE 2018)  
Publisher DOI
10.1117/12.2292882
Scopus ID
2-s2.0-85047302501
Downloading of the abstract is permitted for personal use only. The detection and subsequent correction of motion artifacts is essential for the high diagnostic value of non- invasive coronary angiography using cardiac CT. However, motion correction algorithms have a substantial computational footprint and possible failure modes which warrants a motion artifact detection step to decide whether motion correction is required in the first place. We investigate how accurately motion artifacts in the coronary arteries can be predicted by deep learning approaches. A forward model simulating cardiac motion by creating and integrating artificial motion vector fields in the filtered back projection (FBP) algorithm allows us to generate training data from nine prospectively ECG-triggered high quality clinical cases. We train a Convolutional Neural Network (CNN) classifying 2D motion-free and motion-perturbed coronary cross-section images and achieve a classification accuracy of 94:4% ± 2:9% by four-fold cross-validation.