Abstract
Cardiac magnetic resonance (CMR) imaging is becoming a routine diagnostic and therapy planning tool for some cardiovascular diseases. It is still challenging to properly analyse the acquired data, and the currently available measures do not exploit the rich characteristics of that data. Advanced analysis and modelling techniques are increasingly used to extract additional information from the images, in order to define metrics describing disease manifestations and to quantitatively compare patients. Many techniques share a common bottleneck caused by the image processing required to segment the images and convert the segmentation to a usable computational domain for analysis/modelling. To address this, we present a comprehensive pipeline to go from CMR images to computational bi-ventricle meshes. The latter can be used for biophysical simulations or statistical shape analysis. The provided tutorial describes each step and the proposed pipeline, which makes use of tools that are available open-source. The pipeline was applied to a data-set of myocardial infarction patients, from late gadolinium enhanced CMR images, to analyse and compare structure in these patients. Examples of applications present the use of the output of the pipeline for patient-specific biophysical simulations and population-based statistical shape analysis.
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References
Arevalo, H.J., Vadakkumpadan, F., Guallar, E., Jebb, A., Malamas, P., Wu, K.C., Trayanova, N.A.: Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat. Commun. 7 (2016)
Zhang, X., Cowan, B.R., Bluemke, D.A., Finn, J.P., Fonseca, C.G., Kadish, A.H., Lee, D.C., Lima, J.A., Suinesiaputra, A., Young, A.A., et al.: Atlas-based quantification of cardiac remodeling due to myocardial infarction. PLoS One 9(10), e110243 (2014)
Heiberg, E., Sjgren, J., Ugander, M., Carlsson, M., Engblom, H., Arheden, H.: Design and validation of segment-freely available software for cardiovascular image analysis. BMC Med. Imaging 10(1) (2010)
Heiberg, E., Wigstrom, L., Carlsson, M., Bolger, A., Karlsson, M.: Time resolved three-dimensional automated segmentation of the left ventricle. In: Computers in Cardiology, 2005, pp. 599–602. IEEE (2005)
Engblom, H., Tufvesson, J., Jablonowski, R., Carlsson, M., Aletras, A.H., Hoffmann, P., Jacquier, A., Kober, F., Metzler, B., Erlinge, D., et al.: A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data. J. Cardiovasc. Magn. Reson. 18(1), 1 (2016)
Heiberg, E., Ugander, M., Engblom, H., Gotberg, M., Olivecrona, G.K., Erlinge, D., Arheden, H.: Automated quantification of myocardial infarction from MR images by accounting for partial volume effects: animal, phantom, and human study 1. Radiology 246(2), 581–588 (2008)
Jabbari, R., Engstrøm, T., Glinge, C., Risgaard, B., Jabbari, J., Winkel, B.G., Terkelsen, C.J., Tilsted, H.H., Jensen, L.O., Hougaard, M., et al.: Incidence and risk factors of ventricular fibrillation before primary angioplasty in patients with first st-elevation myocardial infarction: a nationwide study in Denmark. J. Am. Heart Assoc. 4(1), e001399 (2015)
Hergan, K., Schuster, A., Fruhwald, J., Mair, M., Burger, R., Topker, M.: Comparison of left and right ventricular volume measurement using the Simpson’s method and the area length method. Eur. J. Radiol. 65(2), 270–278 (2008)
Arevalo, H., Helm, P., Trayanova, N.: Development of a model of the infarcted canine heart that predicts arrhythmia generation from specific cardiac geometry and scar distribution. In: Computers in Cardiology. IEEE 2008, pp. 497–500 (2008)
Durrleman, S., Pennec, X., Trouvé, A., Ayache, N.: Statistical models of sets of curves and surfaces based on currents. Med. Image Anal. 13, 793–808 (2009)
Gilbert, K., Lam, H.I., Pontré, B., Cowan, B., Occleshaw, C., Liu, J., Young, A.: An interactive tool for rapid biventricular analysis of congenital heart disease. Clin. Physiol. Funct. Imaging (2015)
Pop, M., et al.: EP challenge - STACOM’11: forward approaches to computational electrophysiology using MRI-based models and in-vivo CARTO mapping in swine hearts. In: Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2011. LNCS, vol. 7085, pp. 1–13. Springer, Heidelberg (2012). doi:10.1007/978-3-642-28326-0_1
Acknowledgements
This project was partially funded by the Centre for Cardiological Innovation (CCI), Norway funded by the Norwegian Research Council, and Novo Nordic foundation.
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Marciniak, M. et al. (2017). From CMR Image to Patient-Specific Simulation and Population-Based Analysis: Tutorial for an Openly Available Image-Processing Pipeline. In: Mansi, T., McLeod, K., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2016. Lecture Notes in Computer Science(), vol 10124. Springer, Cham. https://doi.org/10.1007/978-3-319-52718-5_12
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DOI: https://doi.org/10.1007/978-3-319-52718-5_12
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