Abstract
Accurate assessment of left atrial fibrosis in patients with atrial fibrillation relies on high-quality 3D late gadolinium enhancement (LGE) MRI images. However, obtaining such images is challenging due to patient motion, changing breathing patterns, or sub-optimal choice of pulse sequence parameters. Automated assessment of LGE-MRI image diagnostic quality is clinically significant as it would enhance diagnostic accuracy, improve efficiency, ensure standardization, and contributes to better patient outcomes by providing reliable and high-quality LGE-MRI scans for fibrosis quantification and treatment planning. To address this, we propose a two-stage deep-learning approach for automated LGE-MRI image diagnostic quality assessment. The method includes a left atrium detector to focus on relevant regions and a deep network to evaluate diagnostic quality. We explore two training strategies, multi-task learning, and pretraining using contrastive learning, to overcome limited annotated data in medical imaging. Contrastive Learning result shows about 4%, and 9% improvement in F1-Score and Specificity compared to Multi-Task learning when there’s limited data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Colilla, S., Crow, A., Petkun, W., Singer, D.E., Simon, T., Liu, X.: Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population. Am. J. Cardiol. 112(8), 1142–1147 (2013)
ElMaghawry, M., Romeih, S.: DECAAF: emphasizing the importance of MRI in AF ablation. Glob. Cardiol. Sci. Pract. 2015, 8 (2015)
Marrouche, N.F., et al.: Association of atrial tissue fibrosis identified by delayed enhancement MRI and atrial fibrillation catheter ablation: the DECAAF study. JAMA 311(5), 498–506 (2014)
Verma, A., et al.: Approaches to catheter ablation for persistent atrial fibrillation. New England J. Med. 372(19), 1812–1822 (2015). https://doi.org/10.1056/NEJMoa1408288
Oakes, R.S., et al.: Detection and quantification of left atrial structural remodeling with delayed-enhancement magnetic resonance imaging in patients with atrial fibrillation. Circulation 119(13), 1758–1767 (2009)
Caixal, G., et al.: Accuracy of left atrial fibrosis detection with cardiac magnetic resonance: correlation of late gadolinium enhancement with endocardial voltage and conduction velocity. Europace 23(3), 380–388 (2021)
Lange, M., Kwan, E., Dosdall, D.J., MacLeod, R.S., Bunch, T.J., Ranjan, R.: Case report: personalized computational model guided ablation for left atrial flutter. Front. Cardiov. Med. 9, 893752 (2022)
McDowell, K.S., et al.: Methodology for patient-specific modeling of atrial fibrosis as a substrate for atrial fibrillation. J. Electrocardiol. 45(6), 640–645 (2012)
Gräni, C., et al.: Comparison of myocardial fibrosis quantification methods by cardiovascular magnetic resonance imaging for risk stratification of patients with suspected myocarditis. J. Cardiovasc. Magn. Reson. 21, 1–11 (2019)
Flett, A.S., et al.: Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. JACC: Cardiov. Imaging 4(2), 150–156 (2011)
Spiewak, M., et al.: Comparison of different quantification methods of late gadolinium enhancement in patients with hypertrophic cardiomyopathy. Eur. J. Radiol. 74(3), e149–e153 (2010)
Xu, J., et al.: Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 386–395. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_37
Liao, L., et al.: Joint image quality assessment and brain extraction of fetal MRI using deep learning. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 415–424. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_40
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)
Khosla, P., et al.: Supervised contrastive learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 18661–18673 (2020)
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
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Draelos, R.L., Carin, L.: Use HIResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks. arXiv preprint arXiv:2011.08891 (2020)
McInnes, L., Healy, J., Melville, J.: Umap: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)
You, Y., Gitman, I., Ginsburg, B.: Large batch training of convolutional networks. arXiv preprint arXiv:1708.03888 (2017)
Acknowledgements
The National Institutes of Health supported this work under grant numbers R01HL162353.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sultan, K.M.A. et al. (2024). Two-Stage Deep Learning Framework for Quality Assessment of Left Atrial Late Gadolinium Enhanced MRI Images. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers. STACOM 2023. Lecture Notes in Computer Science, vol 14507. Springer, Cham. https://doi.org/10.1007/978-3-031-52448-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-52448-6_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-52447-9
Online ISBN: 978-3-031-52448-6
eBook Packages: Computer ScienceComputer Science (R0)