Reference : FastField: An Open-Source Toolbox for Efficient Approximation of Deep Brain Stimulati...
E-prints/Working papers : Already available on another site
Human health sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/42949
FastField: An Open-Source Toolbox for Efficient Approximation of Deep Brain Stimulation Electric Fields
English
Baniasadi, Mehri mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Proverbio, Daniele mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Goncalves, Jorge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Hertel, Frank mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Husch, Andreas mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
5-Mar-2020
No
[en] Deep Brain Stimulation ; Volume of Tissue Activated ; Electric field
[en] Deep brain stimulation (DBS) is a surgical therapy to alleviate symptoms of certain brain disorders by electrically modulating neural tissues. Computational models predicting electric fields and volumes of tissue activated are key for efficient parameter tuning and network analysis. Currently, we lack efficient and flexible software implementations supporting complex electrode geometries and stimulation settings. Available tools are either too slow (e.g. finite element method–FEM), or too simple, with limited applicability to basic use-cases. This paper introduces FastField, an efficient open-source toolbox for DBS electric field and VTA approximations. It computes scalable e-field approximations based on the principle of superposition, and VTA activation models from pulse width and axon diameter. In benchmarks and case studies, FastField is solved in about 0.2s, ~ 1000 times faster than using FEM. Moreover, it is almost as accurate as using FEM: average Dice overlap of 92%, which is around typical noise levels found in clinical data. Hence, FastField has the potential to foster efficient optimization studies and to support clinical applications
Researchers ; Students ; General public ; Others
http://hdl.handle.net/10993/42949
https://www.biorxiv.org/content/10.1101/2020.03.03.974642v1.full

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