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

T. Elss; H. Nickisch; T. Wissel; H. Schmitt; M. Vembar; M. Morlock; M. Grass

doi:10.1117/12.2292882

2 March 2018 Deep-learning-based CT motion artifact recognition in coronary arteries

T. Elss, H. Nickisch, T. Wissel, H. Schmitt, M. Vembar, M. Morlock, M. Grass

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 1057416 (2018) https://doi.org/10.1117/12.2292882
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

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.

Citation Download Citation

T. Elss, H. Nickisch, T. Wissel, H. Schmitt, M. Vembar, M. Morlock, and M. Grass "Deep-learning-based CT motion artifact recognition in coronary arteries", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057416 (2 March 2018); https://doi.org/10.1117/12.2292882

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE