TY - JOUR
T1 - A fast parallelized computational approach based on sparse LU factorization for predictions of spatial and time-dependent currents and voltages in full-body biomodels
AU - Mishra, Ashutosh
AU - Joshi, Ravindra P.
AU - Schoenbach, Karl H.
AU - Clark, Clifton D.
N1 - Funding Information:
Manuscript received January 7, 2006; revised March 31, 2006. This work was supported in part by the National Training and Information Center (NTIC) and in part by the Air Force Office of Scientific Research (AFOSR), U.S. Air Force.
PY - 2006/8
Y1 - 2006/8
N2 - Realistic and accurate numerical simulations of electrostimulation of tissues and full-body biomodels have been developed and implemented. Typically, whole-body systems are very complex and consist of a multitude of tissues, organs, and subcomponents with diverse properties. From an electrical standpoint, these can be characterized in terms of separate conductivities and permittivities. Accuracy demands good spatial resolution; thus, the overall tissue/animal models need to be discretized into a fine-grained mesh. This can lead to a large number of grid points (especially for a three-dimensional entity) and can place prohibitive requirements of memory storage and execution times on computing machines. Here, the authors include a simple yet fast and efficient numerical implementation. It is based on LU decomposition for execution on a cluster of computers running in parallel with distributed storage of the data in a sparse format. In this paper, the details of electrical tissue representation, the fast algorithm, the relevant biomodels, and specific applications to whole-animal studies of electrostimulation are discussed.
AB - Realistic and accurate numerical simulations of electrostimulation of tissues and full-body biomodels have been developed and implemented. Typically, whole-body systems are very complex and consist of a multitude of tissues, organs, and subcomponents with diverse properties. From an electrical standpoint, these can be characterized in terms of separate conductivities and permittivities. Accuracy demands good spatial resolution; thus, the overall tissue/animal models need to be discretized into a fine-grained mesh. This can lead to a large number of grid points (especially for a three-dimensional entity) and can place prohibitive requirements of memory storage and execution times on computing machines. Here, the authors include a simple yet fast and efficient numerical implementation. It is based on LU decomposition for execution on a cluster of computers running in parallel with distributed storage of the data in a sparse format. In this paper, the details of electrical tissue representation, the fast algorithm, the relevant biomodels, and specific applications to whole-animal studies of electrostimulation are discussed.
KW - Distributed currents
KW - LU decomposition
KW - Parallel computing
KW - Tissue modeling
KW - Whole body
UR - http://www.scopus.com/inward/record.url?scp=33747854517&partnerID=8YFLogxK
U2 - 10.1109/TPS.2006.876485
DO - 10.1109/TPS.2006.876485
M3 - Article
AN - SCOPUS:33747854517
SN - 0093-3813
VL - 34
SP - 1431
EP - 1440
JO - IEEE Transactions on Plasma Science
JF - IEEE Transactions on Plasma Science
IS - 4 II
ER -