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FastField: An open-source toolbox for efficient approximation of deep brain stimulation electric fields.

Mehri Baniasadi1, Daniele Proverbio2, Jorge Gonçalves2

  • 1University of Luxembourg, Luxemburg Center for Systems Biomedicine, Campus Belval, 6 avenue du Swing, L-4367 Belvaux; Centre Hospitalier de Luxembourg, National Department of Neurosurgery, 4 Rue Nicolas Ernest Barblé, L-1210 Luxembourg.

Neuroimage
|September 5, 2020
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Summary

FastField is a new open-source software for deep brain stimulation (DBS) that rapidly and accurately models electric fields and tissue activation. This tool accelerates DBS research and clinical applications by overcoming limitations of existing computational methods.

Keywords:
Deep brain stimulationElectric fieldNeuromodulationSimulationToolboxvolume of tissue activated

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Biomedical Engineering

Background:

  • Deep brain stimulation (DBS) modulates neural tissues for treating brain disorders.
  • Accurate computational models of electric fields and volumes of tissue activated (VTA) are crucial for DBS optimization and analysis.
  • Existing software tools for DBS modeling are often too slow (e.g., finite element method) or too simplistic for complex scenarios.

Purpose of the Study:

  • Introduce FastField, an efficient open-source toolbox for approximating DBS electric fields and VTA.
  • Provide a flexible and scalable solution for complex electrode geometries and stimulation parameters.
  • Enable faster and more accurate computational modeling in DBS research and clinical practice.

Main Methods:

  • Developed FastField using the principle of superposition for scalable electric field approximations.
  • Implemented VTA activation models based on pulse width and axon diameter.
  • Benchmarked FastField against finite element method (FEM) using case studies.

Main Results:

  • FastField achieved computation times approximately 1000 times faster than FEM (around 0.2 seconds).
  • Demonstrated high accuracy comparable to FEM, with an average Dice overlap of 92%.
  • The accuracy is within typical noise levels observed in clinical data.

Conclusions:

  • FastField offers a significant speed improvement over traditional methods like FEM for DBS modeling.
  • The toolbox provides a balance of speed and accuracy, making it suitable for both research and clinical applications.
  • FastField has the potential to facilitate advanced optimization studies and support real-time clinical decision-making in DBS therapy.