title: Parallel implementation of the accelerated BEM approach for EMSI of the human brain.
abstract: Boundary element method (BEM) is one of the numerical methods which is commonly used to solve the forward problem (FP) of electro-magnetic source imaging with realistic head geometries. Application of BEM generates large systems of linear equations with dense matrices. Generation and solution of these matrix equations are time and memory consuming. This study presents a relatively cheap and effective solution for parallel implementation of the BEM to reduce the processing times to clinically acceptable values. This is achieved using a parallel cluster of personal computers on a local area network. We used eight workstations and implemented a parallel version of the accelerated BEM approach that distributes the computation and the BEM matrix efficiently to the processors. The performance of the solver is evaluated in terms of the CPU operations and memory usage for different number of processors. Once the transfer matrix is computed, for a 12,294 node mesh, a single FP solution takes 676 ms on a single processor and 72 ms on eight processors. It was observed that workstation clusters are cost effective tools for solving the complex BEM models in a clinically acceptable time.
authors.affiliation: Department of Electrical and Electronics Engineering, Brain Research Laboratory, Middle East Technical University, 06531, Ankara, Turkey. yoldas@eee.metu.edu.tr
authors.forename: Y
authors.initials: Y
authors.lastname: Ataseven
authors.forename: Z
authors.initials: Z
authors.lastname: Akalin-Acar
authors.forename: C E
authors.initials: CE
authors.lastname: Acar
authors.forename: N G
authors.initials: NG
authors.lastname: Gençer
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006257:Head / Q000033:anatomy & histology; D006801:Humans; D008122:Local Area Networks; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography / Q000379:methods*; D008953:Models, Anatomic; D008959:Models, Neurological; D012815:Signal Processing, Computer-Assisted