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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

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Orthogonal recursive bisection as data decomposition strategy for massively parallel cardiac simulations.

Matthias Reumann¹, Blake G Fitch, Aleksandr Rayshubskiy

¹IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA. mreumann@ieee.org

Biomedizinische Technik. Biomedical Engineering

|June 11, 2011

Summary

The orthogonal recursive bisection algorithm enables efficient, large-scale cardiac electrophysiology simulations. This method achieves near-perfect speedup, significantly reducing computation time for heart models.

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Area of Science:

Computational biology
Biophysics
High-performance computing

Background:

Cardiac electrophysiology models are crucial for research and clinical applications.
Simulating whole-heart electrical activity requires significant computational resources.

Purpose of the Study:

To introduce and evaluate the orthogonal recursive bisection algorithm for hierarchical segmentation of anatomical models.
To demonstrate strong scaling of cardiac electrophysiology simulations on supercomputers.

Main Methods:

Hierarchical segmentation of anatomical models using orthogonal recursive bisection.
Electrophysiology simulations using FitzHugh-Nagumo (FHN) and ten Tusscher (TT04) models with monodomain diffusion.
Benchmark simulations on IBM Blue Gene/P and L supercomputers up to 32,768 cores.

Main Results:

The algorithm achieved good load balancing and near-linear speedup across a large number of cores.
A 1000 ms full heart beat simulation using the FHN model completed in approximately 6.5 minutes on 32,768 cores.
Demonstrated strong scaling to significantly higher core counts than previously reported for organ-level heart simulations.

Conclusions:

The orthogonal recursive bisection algorithm enables highly scalable cardiac simulations.
Reduced simulation runtimes can facilitate wider adoption of cardiac models in research and clinical settings.
This approach significantly advances the computational feasibility of detailed cardiac modeling.