Cardiac Fibers Estimation from Arbitrarily Spaced Diffusion Weighted MRI

  • Andreas Nagler
  • Cristóbal BertoglioEmail author
  • Christian T. Stoeck
  • Sebastian Kozerke
  • Wolfgang A. Wall
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)


We propose a framework for estimating fiber fields in the heart from arbitrarily spaced diffusion weighted MRI. The approach is based on a parametric and space-dependent mathematical representation of the helix angles across the heart, leading to a semi-analytical formula of the diffusion tensor, without any particular assumption on the ventricular shape. Then, by solving an nonlinear inverse problem, the degrees of freedom of the model can be estimated from measured diffusion weighted data. We illustrate the methodology using synthetic data and compare it with previously reported fiber reconstruction techniques.


Fiber Orientation Helix Angle Prolate Spheroid Diffusion Tensor Magnetic Resonance Image Fiber Family 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The results presented in this article are part of the Advanced Cardiac Mechanics Emulator, an initiative supported by the Institute for Advanced Study (TU München). This support is gratefully acknowledged. We also thank Radomír Chabiniok and Jack Harmer (King’s College London) for the valuable discussions.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andreas Nagler
    • 1
  • Cristóbal Bertoglio
    • 1
    • 2
    Email author
  • Christian T. Stoeck
    • 3
    • 4
  • Sebastian Kozerke
    • 3
    • 4
  • Wolfgang A. Wall
    • 1
  1. 1.Institute for Computational MechanicsTechnische Universität MünchenMunichGermany
  2. 2.Center for Mathematical ModelingUniversidad de ChileSantiagoChile
  3. 3.Institute for Biomedical EngineeringUniversity and ETH ZurichZurichSwitzerland
  4. 4.Division of Imaging Sciences and Biomedical EngineeringKing’s College of LondonLondonUK

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