Automatic identification of patients with functionally significant obstructive coronary artery disease using non-invasive cardiac CT

Coronary artery disease (CAD) remains the first cause of morbidity and mortality in the Western world and it is expected that this trend will continue in the coming years. In clinical routine, patients with CAD are increasingly identified using non-invasive coronary CT angiography (CCTA), a non-invasive imaging tool for detection and exclusion of the obstructive coronary artery stenosis. Despite its high sensitivity, CCTA is currently not capable of determining the functional significance of the detected stenosis. Therefore, after undergoing CCTA, many patients undergo invasive coronary angiography (ICA).

In this project, we design a quantitative method to determine which coronary artery stenoses as seen on CCTA images are functionally significant, and thereby to identify patients who need to undergo invasive coronary catheterization and spare those who do not.

The video below describes our method for coronary calcium scoring in contrast-enhanced cardiac CT, which was presented at MICCAI 2015.



A slice from contrast enhanced cardiac CT scan (left) and corresponding slice from cardiac CT scan without contrast enhancement. Areas indicated in blue show calcified plaque in the left coronary artery.


Researchers


Journal Articles

1.

M. Zreik, R.W. van Hamersvelt, N. Khalili, J.M. Wolterink, M. Voskuil, M.A. Viergever, T. Leiner, I. Išgum

Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography Journal Article

Transactions on Medical Imaging, 39 (5), pp. 1545-1557, 2020.

Abstract | Links | BibTeX

2.

J.M. Wolterink, R.W. van Hamersvelt, M.A. Viergever, T. Leiner, I. Išgum

Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier Journal Article

Medical Image Analysis, 51 , pp. 46-60, 2019.

Abstract | Links | BibTeX

3.

M. Zreik, R.W. van Hamersvelt, J.M. Wolterink, T. Leiner, M.A. Viergever, I. Išgum

A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography Journal Article

IEEE Transactions on Medical Imaging, 38 (7), 2018.

Abstract | Links | BibTeX

4.

R.W. van Hamersvelt*, M. Zreik*, M. Voskuil, M.A. Viergever, I. Išgum, T. Leiner

Deep learning analysis of left ventricular myocardium in CT angiographic intermediate degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis Journal Article

European Radiology, 29 (5), pp. 2350–2359, 2018, (*equal contribution).

Abstract | Links | BibTeX

5.

M. Zreik, N. Lessmann, R.W. van Hamersvelt, J.M. Wolterink, M. Voskuil, M.A. Viergever, T. Leiner, I. Išgum

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis Journal Article

Medical Image Analysis, 44 , pp. 72-85, 2018.

Abstract | Links | BibTeX

6.

J.M. Wolterink, T. Leiner, M.A. Viergever, I. Isgum

Generative adversarial networks for noise reduction in low-dose CT Journal Article

IEEE Transactions on Medical Imaging, 36 (12), pp. 2536 - 2545, 2017.

Abstract | Links | BibTeX

7.

J.M. Wolterink, T. Leiner, B.D. de Vos, R.W. van Hamersvelt, M.A. Viergever, I. Isgum

Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks Journal Article

Medical Image Analysis, 34 , pp. 123-136, 2016.

Abstract | BibTeX

Inproceedings

1.

M. Zreik, N. Hampe, T. Leiner, N. Khalili, J.M. Wolterink, M. Voskuil, M.A. Viergever, I. Išgum

Combined analysis of coronary arteries and the left ventricular myocardium in cardiac CT angiography for detection of patients with functionally significant stenosis Inproceedings

In: SPIE Medical Imaging, pp. 115961F, 2021.

Abstract | Links | BibTeX

2.

M. Zreik, R.W. van Hamersvelt, J.M. Wolterink, T. Leiner, M.A. Viergever, I. Išgum

Automatic Detection and Characterization of Coronary Artery Plaque and Stenosis using a Recurrent Convolutional Neural Network in Coronary CT Angiography Inproceedings

In: Medical Imaging with Deep Learning (MIDL 2018), 2018.

Abstract | Links | BibTeX

3.

J.M. Wolterink, T. Leiner, M.A. Viergever, I. Išgum

Automatic segmentation and disease classification using cardiac cine MR images Inproceedings

In: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017, pp. 101-110, Springer, Cham, 2018.

Abstract | Links | BibTeX

4.

J.M. Wolterink, T. Leiner, M.A. Viergever, I. Isgum

Dilated convolutional neural networks for cardiovascular MR segmentation in congenital heart disease Inproceedings

In: M.A. Zuluaga; K. Bhatia; B. Kainz; M.H. Moghari; D.F. Pace (Ed.): HVSMR 2016: MICCAI Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, pp. 95-102, Springer International Publishing, 2017.

Abstract | BibTeX

5.

P. Moeskops, J.M. Wolterink, B.H.M. van der Velden, K.G.A. Gilhuijs, T. Leiner, M.A. Viergever, I. Isgum

Deep learning for multi-task medical image segmentation in multiple modalities Inproceedings

In: Medical Image Computing and Computer-Assisted Intervention, pp. 478-486, 2016.

Abstract | BibTeX

6.

M. Zreik; T. Leiner; B.D. de Vos; R.W. van Hamersvelt; M.A. Viergever; I. Isgum

Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks Inproceedings

In: IEEE International Symposium on Biomedical Imaging, pp. pp. 40-43, 2016.

BibTeX

7.

J.M. Wolterink, T. Leiner, M.A. Viergever, I. Isgum

Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks Inproceedings

In: N. Navab, J. Hornegger, W.M. Wells, A.F. Frangi (Ed.): Medical Image Computing and Computer-Assisted Intervention, pp. 589-596, Springer International Publishing, 2015.

Abstract | BibTeX

Abstracts

1.

R.W. van Hamersvelt, M. Zreik, M. Voskuil, I. Išgum, T. Leiner

Deep learning-based analysis of the left ventricular myocardium in coronary CTA images improves specificity for detection of functionally significant coronary artery stenosis Abstract

In: 2018.

Abstract | BibTeX

2.

R. van Hamersvelt, M. Zreik, N. Lessmann, J. Wolterink, M. Voskuil, M.A Viergever, T. Leiner, I. Isgum

Improving Specificity of Coronary CT Angiography for the Detection of Functionally Significant Coronary Artery Disease: A Deep Learning Approach Abstract

In: 2017.

Abstract | BibTeX

3.

M. Zreik, N. Lessmann, R. van Hamersvelt, J. Wolterink, M. Voskuil, M.A Viergever, T. Leiner, I. Isgum

Deep learning analysis of the left ventricular myocardium in cardiac CT images enables detection of functionally significant coronary artery stenosis regardless of coronary anatomy Abstract

In: 2017.

Abstract | BibTeX

4.

J.M. Wolterink, T. Leiner, M.A. Viergever, I. Isgum

An adversarial deep learning approach to coronary CT radiation reduction Abstract

In: 2017.

Abstract | BibTeX

PhD Theses

1.

M. Zreik

Machine learning for coronary artery disease analysis in cardiac CT PhD Thesis

Utrecht University, The Netherlands, 2020, ISBN: 978-94-6323-978-3.

BibTeX