Towards real time diffuse optical tomography: Accelerating light propagation modeling employing parallel computing on GPU and CPU

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Towards real time diffuse optical tomography: Accelerating light propagation modeling employing parallel computing on GPU and CPU. / Doulgerakis, Matthaios; Eggebrecht, Adam T; Wojtkiewicz, Stanislaw; Culver, Joseph; Dehghani, Hamid.

In: Journal of Biomedical Optics, Vol. 22, No. 12, 125001 , 01.12.2017.

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@article{9cededcf4bbf4aec90ab87b4e180f89f,
title = "Towards real time diffuse optical tomography:: Accelerating light propagation modeling employing parallel computing on GPU and CPU",
abstract = "Parameter recovery in diffuse optical tomography is a computationally expensive algorithm, especially when used for large and complex volumes, as in the case of human brain functional imaging. The modeling of light propagation, also known as the forward problem, is the computational bottleneck of the recovery algorithm, whereby the lack of a real-time solution is impeding practical and clinical applications. The objective of this work is the acceleration of the forward model, within a Diffusion Approximation based finite element modeling framework, employing parallelization to expedite the calculation of light propagation in realistic adult head models. The proposed methodology is applicable for modelling both continuous wave and frequency domain systems with the results demonstrating a tenfold speed increase when GPU architectures are available, whilst maintaining high accuracy. It is shown that for a very high resolution finite element model of the adult human head with ~600,000 nodes, consisting of heterogeneous layers, light propagation can be calculated at ~0.25 seconds per excitation source.",
keywords = "Diffuse Optical Tomography, Finite Element Method, NIRFAST, Parallel Computing, GPU",
author = "Matthaios Doulgerakis and Eggebrecht, {Adam T} and Stanislaw Wojtkiewicz and Joseph Culver and Hamid Dehghani",
year = "2017",
month = dec,
day = "1",
doi = "10.1117/1.JBO.22.12.125001",
language = "English",
volume = "22",
journal = "Journal of Biomedical Optics",
issn = "1083-3668",
publisher = "Society of Photo-Optical Instrumentation Engineers",
number = "12",

}

RIS

TY - JOUR

T1 - Towards real time diffuse optical tomography:

T2 - Accelerating light propagation modeling employing parallel computing on GPU and CPU

AU - Doulgerakis, Matthaios

AU - Eggebrecht, Adam T

AU - Wojtkiewicz, Stanislaw

AU - Culver, Joseph

AU - Dehghani, Hamid

PY - 2017/12/1

Y1 - 2017/12/1

N2 - Parameter recovery in diffuse optical tomography is a computationally expensive algorithm, especially when used for large and complex volumes, as in the case of human brain functional imaging. The modeling of light propagation, also known as the forward problem, is the computational bottleneck of the recovery algorithm, whereby the lack of a real-time solution is impeding practical and clinical applications. The objective of this work is the acceleration of the forward model, within a Diffusion Approximation based finite element modeling framework, employing parallelization to expedite the calculation of light propagation in realistic adult head models. The proposed methodology is applicable for modelling both continuous wave and frequency domain systems with the results demonstrating a tenfold speed increase when GPU architectures are available, whilst maintaining high accuracy. It is shown that for a very high resolution finite element model of the adult human head with ~600,000 nodes, consisting of heterogeneous layers, light propagation can be calculated at ~0.25 seconds per excitation source.

AB - Parameter recovery in diffuse optical tomography is a computationally expensive algorithm, especially when used for large and complex volumes, as in the case of human brain functional imaging. The modeling of light propagation, also known as the forward problem, is the computational bottleneck of the recovery algorithm, whereby the lack of a real-time solution is impeding practical and clinical applications. The objective of this work is the acceleration of the forward model, within a Diffusion Approximation based finite element modeling framework, employing parallelization to expedite the calculation of light propagation in realistic adult head models. The proposed methodology is applicable for modelling both continuous wave and frequency domain systems with the results demonstrating a tenfold speed increase when GPU architectures are available, whilst maintaining high accuracy. It is shown that for a very high resolution finite element model of the adult human head with ~600,000 nodes, consisting of heterogeneous layers, light propagation can be calculated at ~0.25 seconds per excitation source.

KW - Diffuse Optical Tomography

KW - Finite Element Method

KW - NIRFAST

KW - Parallel Computing

KW - GPU

U2 - 10.1117/1.JBO.22.12.125001

DO - 10.1117/1.JBO.22.12.125001

M3 - Article

VL - 22

JO - Journal of Biomedical Optics

JF - Journal of Biomedical Optics

SN - 1083-3668

IS - 12

M1 - 125001

ER -