From CMR Image to Patient-Specific Simulation and Population-Based Analysis: Tutorial for an Openly Available Image-Processing Pipeline

  • Maciej Marciniak
  • Hermenegild Arevalo
  • Jacob Tfelt-Hansen
  • Thomas Jespersen
  • Reza Jabbari
  • Charlotte Glinge
  • Kiril A. Ahtarovski
  • Niels Vejlstrup
  • Thomas Engstrom
  • Mary M. Maleckar
  • Kristin McLeod
Conference paper

DOI: 10.1007/978-3-319-52718-5_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10124)
Cite this paper as:
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

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.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Maciej Marciniak
    • 1
  • Hermenegild Arevalo
    • 1
  • Jacob Tfelt-Hansen
    • 2
  • Thomas Jespersen
    • 3
  • Reza Jabbari
    • 2
  • Charlotte Glinge
    • 2
  • Kiril A. Ahtarovski
    • 2
  • Niels Vejlstrup
    • 2
  • Thomas Engstrom
    • 2
  • Mary M. Maleckar
    • 1
  • Kristin McLeod
    • 1
    • 4
  1. 1.Cardiac Modelling DepartmentSimula Research LaboratoryOsloNorway
  2. 2.Department of CardiologyRigshospitaletCopenhagenDenmark
  3. 3.Department of Biomedical SciencesUniversity of CopenhagenCopenhagenDenmark
  4. 4.Centre for Cardiological InnovationOsloNorway

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