Abstract
Patient-specific modeling of blood flow combining CT image data and computational fluid dynamics has significant potential for assessing the functional significance of coronary artery disease. An accurate segmentation of the coronary arteries, an essential ingredient for blood flow modeling methods, is currently attained by a combination of automated algorithms with human review and editing. However, not all portions of the coronary artery tree affect blood flow and pressure equally, and it is of significant importance to direct human review and editing towards regions that will most affect the subsequent simulations. We present a data-driven approach for real-time estimation of sensitivity of blood-flow simulations to uncertainty in lumen segmentation. A machine learning method is used to map patient-specific features to a sensitivity value, using a large database of patients with precomputed sensitivities. We validate the results of the machine learning algorithm using direct 3D blood flow simulations and demonstrate that the algorithm can predict sensitivities in real time with only a small reduction in accuracy as compared to the 3D solutions. This approach can also be applied to other medical applications where physiologic simulations are performed using patient-specific models created from image data.
Chapter PDF
Similar content being viewed by others
References
Taylor, C.A., Fonte, T.A., Min, J.K.: Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. JACC 61(22), 2233–2241 (2013)
Koo, B.K., et al.: Diagnosis of Ischemia-Causing Coronary Stenoses by Noninvasive Fractional Flow Reserve Computed From Coronary Computed Tomographic Angiograms Results From the Prospective Multicenter DISCOVER-FLOW Study. JACC 58(19), 1989–1997 (2011)
Min, J.K., et al.: Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. Journal of American Medical Association 308(12), 1237–1245 (2012)
Norgaard, B.L., et al.: Diagnostic performance of non-invasive fractional flow reserve derived from coronary CT angiography in suspected coronary artery disease: The NXT trial. Journal of American College of Cardiology (2014), doi:10.1016.11.043
Schaap, M., et al.: Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Medical Image Analysis 13(5), 701–714 (2009)
Lesage, D., et al.: A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes. Medical Image Analysis 13(6), 819–845 (2009)
Karmonik, C., Brown, A., Debus, K., Bismuth, J., Lumsden, A.B.: CFD Challenge: Predicting Patient-Specific Hemodynamics at Rest and Stress through an Aortic Coarctation. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2013. LNCS, vol. 8330, pp. 94–101. Springer, Heidelberg (2014)
Sankaran, S., Grady, L.J., Taylor, C.A.: Fast geometric sensitivity analysis in hemodynamic simulations using machine learning. Journal of Computational Physics (2014) (under review)
Sankaran, S., Marsden, A.L.: A stochastic collocation method for uncertainty quantification and propagation in cardiovascular simulations. Journal of Biomechanical Engineering 133, 031001-1 (2011)
Shahzad, R., et al.: Detection and Quantification of Coronary Artery Stenoses on CTA. International Journal of Cardiovascular Imaging 29, 1–13 (2013)
Hall, M., et al.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sankaran, S., Grady, L.J., Taylor, C.A. (2014). Real-Time Sensitivity Analysis of Blood Flow Simulations to Lumen Segmentation Uncertainty. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham. https://doi.org/10.1007/978-3-319-10470-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-10470-6_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10469-0
Online ISBN: 978-3-319-10470-6
eBook Packages: Computer ScienceComputer Science (R0)