Artificial neural networks for retrieving absorption and reduced scattering spectra from frequency-domain diffuse reflectance spectroscopy at short source-detector separation

Yu Wen Chen, Chien Chih Chen, Po Jung Huang, Sheng Hao Tseng

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Diffuse reflectance spectroscopy (DRS) based on the frequencydomain (FD) technique has been employed to investigate the optical properties of deep tissues such as breast and brain using source to detector separation up to 40 mm. Due to the modeling and system limitations, efficient and precise determination of turbid sample optical properties from the FD diffuse reflectance acquired at a source-detector separation (SDS) of around 1 mm has not been demonstrated. In this study, we revealed that at SDS of 1 mm, acquiring FD diffuse reflectance at multiple frequencies is necessary for alleviating the influence of inevitable measurement uncertainty on the optical property recovery accuracy. Furthermore, we developed artificial neural networks (ANNs) trained by Monte Carlo simulation generated databases that were capable of efficiently determining FD reflectance at multiple frequencies. The ANNs could work in conjunction with a least-square optimization algorithm to rapidly (within 1 second), accurately (within 10%) quantify the sample optical properties from FD reflectance measured at SDS of 1 mm. In addition, we demonstrated that incorporating the steady-state apparatus into the FD DRS system with 1 mm SDS would enable obtaining broadband absorption and reduced scattering spectra of turbid samples in the wavelength range from 650 to 1000 nm.

Original languageEnglish
Pages (from-to)1496-1510
Number of pages15
JournalBiomedical Optics Express
Volume7
Issue number4
DOIs
Publication statusPublished - 2016 Mar 24

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All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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