High-Speed Fluorescence Molecular Tomography Reconstructions through a Sparsity Constrained Neural Network

Fay Wang, Andreas H. Hielscher, Stephen Hyunkeol Kim

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


Fluorescence molecular tomography (FMT) has gained prominence in recent years as a viable optical imaging technique for non-invasive, high-sensitivity, tomographic imaging of the brain. While optical imaging methods have demonstrated promising results for quantitative imaging of functional changes in the brain, they are still limited in their abilities to achieve high spatial and temporal resolution. To address these challenges, we present here a deep learning solution for FMT reconstructions, which implements a neural network with our novel asymptotic sparse function from our previously introduced sensitivity equation-based non-iterative sparse optical reconstruction (SENSOR) code to achieve high-resolution and sparse reconstructions using only learned parameters. We evaluate the proposed network through numerical phantom experiments. Furthermore, once the network is trained, the total reconstruction time is independent of the number of sources and wavelengths used.

Original languageEnglish (US)
Title of host publicationHigh-Speed Biomedical Imaging and Spectroscopy VIII
EditorsKevin K. Tsia, Keisuke Goda
ISBN (Electronic)9781510658851
StatePublished - 2023
EventHigh-Speed Biomedical Imaging and Spectroscopy VIII 2023 - San Francisco, United States
Duration: Jan 28 2023Jan 30 2023

Publication series

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


ConferenceHigh-Speed Biomedical Imaging and Spectroscopy VIII 2023
Country/TerritoryUnited States
CitySan Francisco


  • Optical imaging
  • fluorescence molecular tomography
  • neural network
  • sparsity
  • time domain

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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