From CMR Image to Patient-Specific Simulation and Population-Based Analysis: Tutorial for an Openly Available Image-Processing Pipeline
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.
KeywordsCardiac Magnetic Resonance Right Ventricle Late Gadolinium Enhancement Cardiac Magnetic Resonance Image Late Gadolinium Enhancement Image
This project was partially funded by the Centre for Cardiological Innovation (CCI), Norway funded by the Norwegian Research Council, and Novo Nordic foundation.
- 1.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)Google Scholar
- 3.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)Google Scholar
- 4.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)Google Scholar
- 5.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)CrossRefGoogle Scholar
- 7.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)CrossRefGoogle Scholar
- 9.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)Google Scholar
- 11.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)Google Scholar
- 12.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 CrossRefGoogle Scholar