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Progress on Customization of Predictive MRI-Based Macroscopic Models from Experimental Data

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Abstract

MR image-based computer heart models are powerful non-invasive tools that can help us predict the transmural electrical propagation of abnormal depolarization-repolarization waves in the presence of infarct scars (i.e., collagenous fibrosis), a major cause of sudden death; however, an important step is the customization of these models from electrophysiology studies (EP) . In this work, we used MR-EP data obtained in a pre-clinical animal model (i.e., three healthy and two infarcted swine hearts) and customized a simple mono-domain model (i.e., the Aliev-Panfilov model). Specifically, we estimated the mathematical parameters corresponding to: a) the repolarization phase fromin vivo activation-recovery intervals, ARIs (recordedin vivo with a CARTO system), and b) the anisotropy ratio (from fluorescence microscopic imaging of connexin 43, Cx43). Our measurements showed that in the ischemic peri-infarct areas the ARIs intervals were shorter by ~ 14% compared to those in normal tissue, and that there was a significant reduction (> 50%) in the Cx43 density (which tunes the cell-to-cell coupling and tissue bulk conductivity) with respect to both longitudinal and transverse directions of the myocyte. In addition, we included comparisons between virtualin silico simulations of activation maps obtained with different parameters used as input to a 3D MR-based biventricular model. Our preliminary results demonstrated the feasibility of usinggeneric parameters to customize such MR-based models; however, further quantitative studies are needed. Finally, we discussed the overall advantages and limitations of our simplified approach, along with future directions.

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Author information

Authors and Affiliations

  1. Sunnybrook Research Institute, Toronto, Canada

    Mihaela Pop, Samuel Oduneye, Sudip Ghate, Labonny Biswas, Roey Flor, Eugene Crystal & Graham A. Wright

  2. University of Toronto, Canada

    Mihaela Pop, Samuel Oduneye, Susan Newbigging, Eugene Crystal & Graham A. Wright

  3. Inria - Asclepios Project, Sophia Antipolis, France

    Maxime Sermesant & Nicholas Ayache

  4. CMHD Pathology Core, Toronto, Canada

    Susan Newbigging

Authors
  1. Mihaela Pop

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  2. Maxime Sermesant

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  3. Samuel Oduneye

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  4. Sudip Ghate

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  5. Labonny Biswas

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  6. Roey Flor

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  7. Susan Newbigging

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  8. Eugene Crystal

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  9. Nicholas Ayache

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  10. Graham A. Wright

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Editor information

Editors and Affiliations

  1. Universitat Pompeu Fabra, Barcelona, Spain

    Oscar Camara

  2. Siemens Corporation, Corporate Technology, Princeton, NJ, USA

    Tommaso Mansi

  3. University of Toronto, ON, Canada

    Mihaela Pop

  4. King’s College London, UK

    Kawal Rhode

  5. Inria, Sophia Antipolis, France

    Maxime Sermesant

  6. University of Auckland, New Zealand

    Alistair Young

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© 2014 Springer-Verlag Berlin Heidelberg

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Pop, M.et al. (2014). Progress on Customization of Predictive MRI-Based Macroscopic Models from Experimental Data. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2013. Lecture Notes in Computer Science, vol 8330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54268-8_18

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JPY 5262
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