1 December 2017 Toward real-time diffuse optical tomography: accelerating light propagation modeling employing parallel computing on GPU and CPU
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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 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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Matthaios Doulgerakis-Kontoudis, Matthaios Doulgerakis-Kontoudis, Adam T. Eggebrecht, Adam T. Eggebrecht, Stanislaw Wojtkiewicz, Stanislaw Wojtkiewicz, Joseph P. Culver, Joseph P. Culver, Hamid Dehghani, Hamid Dehghani, } "Toward real-time diffuse optical tomography: accelerating light propagation modeling employing parallel computing on GPU and CPU," Journal of Biomedical Optics 22(12), 125001 (1 December 2017). https://doi.org/10.1117/1.JBO.22.12.125001 . Submission: Received: 28 June 2017; Accepted: 6 November 2017
Received: 28 June 2017; Accepted: 6 November 2017; Published: 1 December 2017
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