Employing a neural network approach for reducing the convergence speed of diffuse optical image reconstruction algorithms

Fay Wang, Andreas H. Hielscher, Stephen H. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Time-domain tomographic image reconstruction is typically based on an iterative process that requires repeated solving of the forward model of time-dependent light propagation in tissue. As a result, image reconstruction times remain relatively high. This has been one of the main obstacles in the practical use of time-domain data, for example, for realtime monitoring of brain function, in which case results have to be displayed in less than a second. To overcome this problem, we have developed a neural-network-based approach that promises to deliver image reconstructions in the subseconds range. The inputs to this network are parameterized data derived from the Mellin and Laplace transforms of the time of flight (ToF) distribution. In this study, we specifically focused on three data types: the integrated intensity (E), the mean time of flight (<t<), and the exponential feature (L). The network tested consisted of an input layer, three hidden layers, and an output layer that represents the spatial distribution of absorption values for the medium. We trained the parameters of the network with simulated brain diffuse optical tomography data. The inverse problem is then solved with a single-feed forward pass through the network. We demonstrate that this network, once trained, can recover single and multiple inclusions in a 3D medium with accurate localization within milliseconds and outperforms constrained iterative reconstruction methods.

Original languageEnglish (US)
Title of host publicationHigh-Speed Biomedical Imaging and Spectroscopy VII
EditorsKevin K. Tsia, Keisuke Goda
ISBN (Electronic)9781510648135
StatePublished - 2022
EventHigh-Speed Biomedical Imaging and Spectroscopy VII 2022 - Virtual, Online
Duration: Feb 20 2022Feb 24 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceHigh-Speed Biomedical Imaging and Spectroscopy VII 2022
CityVirtual, Online


  • brain imaging
  • deep learning
  • diffuse optical tomography
  • neural networks

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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