Abstract
Cardiac magnetic resonance (CMR) is the gold standard for quantification of cardiac volumes, function, and blood flow. Tailored MR pulse sequences define the contrast mechanisms, acquisition geometry and timing which can be applied during CMR to achieve unique tissue characterisation. It is impractical for each patient to have every possible acquisition option. We target the aortic valve in the three-chamber (3-CH) cine CMR view. Two major types of anomalies are possible in the aortic valve. Stenosis: the narrowing of the valve which prevents an adequate outflow of blood, and insufficiency (regurgitation): the inability to stop the back-flow of blood into the left ventricle. We develop and evaluate a deep learning system to accurately classify aortic valve abnormalities to enable further directed imaging for patients who require it. Inspired by low level image processing tasks, we propose a multi-level network that generates heat maps to locate the aortic valve leaflets’ hinge points and aortic stenosis or regurgitation jets. We trained and evaluated all our models on a dataset of clinical CMR studies obtained from three NHS hospitals (n = 1,017 patients). Our results (mean accuracy = 0.93 and F1 score = 0.91), show that an expert-guided deep learning-based feature extraction and a classification model provide a feasible strategy for prescribing further, directed imaging, thus improving the efficiency and utility of CMR scanning.
K. Vimalesvaran and F. Uslu—contributed equally.
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References
Myerson, S.G.: CMR in evaluating valvular heart disease: diagnosis, severity, and outcomes. Cardiovasc. Imaging 14(10), 2020–2032 (2021)
Baumgartner, H.: What influences the outcome of valve replacement in critical aortic stenosis? Heart 91(10), 1254 (2005)
Thubrikar, M.: The Aortic Valve. Routledge, Abingdon (2018)
Howard, J.P., et al.: Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition. Int. J. Cardiovasc. Imaging 37(3), 1033–1042 (2020). https://doi.org/10.1007/s10554-020-02050-w
Kramer, C.M., Barkhausen, J., Bucciarelli-Ducci, C., Flamm, S.D., Kim, R.J., Nagel, E.: Standardized cardiovascular magnetic resonance imaging (cmr) protocols: 2020 update. J. Cardiovasc. Magn. Reson. 22(1), 1–18 (2020)
Lin, A., Kolossváry, M., Išgum, I., Maurovich-Horvat, P., Slomka, P.J., Dey, D.: Artificial intelligence: improving the efficiency of cardiovascular imaging. Expert Rev. Med. Dev. 17(6), 565–577 (2020)
Gonzales, R.A., Lamy, J., Seemann, F., Heiberg, E., Onofrey, J.A., Peters, D.C.: TVnet: automated time-resolved tracking of the tricuspid valve plane in MRI long-axis cine images with a dual-stage deep learning pipeline. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 567–576. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_55
Petch, J., Di, S., Nelson, W.: Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can. J. Cardiol. 38, 204–213 (2021)
Fries, J.A., et al.: Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences. Nat. Commun. 10(1), 1–10 (2019)
Guala, A., et al.: Machine learning to automatically detect anatomical landmarks on phase-contrast enhanced magnetic resonance angiography. Eur. Heart J.-Cardiovasc. Imaging 22(Supplement_2), jeab090-122 (2021)
Mejia Cordova, M., et al.: Reinforcement machine learning-based aortic anatomical landmarks detection from phase-contrast enhanced magnetic resonance angiography. Eur. Heart J.-Cardiovasc. Imaging 22(Supplement_1), jeaa356-286 (2021)
Ebbers, T.: Flow imaging: cardiac applications of 3D cine phase-contrast MRI. Curr. Cardiovasc. Imaging Rep. 4(2), 127–133 (2011)
Johnson, E.M., et al.: Detecting aortic valve-induced abnormal flow with seismocardiography and cardiac MRI. Ann. Biomed. Eng. 48(6), 1779–1792 (2020)
Ho,T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Acknowledgements
This work was supported by the UKRI CDT in AI for Healthcare http://ai4health.io (Grant No. EP/S023283/1)
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Vimalesvaran, K. et al. (2022). Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_54
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