TY - JOUR
T1 - Toward real-time diffuse optical tomography
T2 - Accelerating light propagation modeling employing parallel computing on GPU and CPU
AU - Doulgerakis, Matthaios
AU - Eggebrecht, Adam
AU - Wojtkiewicz, Stanislaw
AU - Culver, Joseph
AU - Dehghani, Hamid
N1 - Funding Information:
This work has been funded by the National Institutes of Health under Grant Nos. R01NS090874-08, RO1-CA132750, K01MH103594, and R21MH109775.
Publisher Copyright:
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
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 modeling both continuous wave and frequency-domain systems with the results demonstrating a 10-fold speed increase when GPU architectures are available, while 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 s/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 modeling both continuous wave and frequency-domain systems with the results demonstrating a 10-fold speed increase when GPU architectures are available, while 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 s/excitation source.
KW - GPU
KW - NIRFAST
KW - diffuse optical tomography
KW - finite-element method
KW - parallel computing
UR - http://www.scopus.com/inward/record.url?scp=85042619941&partnerID=8YFLogxK
U2 - 10.1117/1.JBO.22.12.125001
DO - 10.1117/1.JBO.22.12.125001
M3 - Article
C2 - 29197176
AN - SCOPUS:85042619941
VL - 22
JO - Journal of Biomedical Optics
JF - Journal of Biomedical Optics
SN - 1083-3668
IS - 12
M1 - 125001
ER -