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

Matthaios Doulgerakis, Adam Eggebrecht, Stanislaw Wojtkiewicz, Joseph Culver, Hamid Dehghani

Research output: Contribution to journalArticle

9 Scopus citations

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.

Original languageEnglish
Article number125001
JournalJournal of biomedical optics
Volume22
Issue number12
DOIs
StatePublished - Dec 1 2017

Keywords

  • GPU
  • NIRFAST
  • diffuse optical tomography
  • finite-element method
  • parallel computing

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