Quantitative molecular sensing in biological tissues: An approach to non-invasive optical characterization

Malavika Chandra, Karthik Vishwanath, Greg D. Fichter, Elly Liao, Scott J. Hollister, Mary Ann Mycek

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

A method to non-invasively and quantitatively characterize thick biological tissues by combining both experimental and computational approaches in tissue optical spectroscopy was developed and validated on fifteen porcine articular cartilage (AC) tissue samples. To the best of our knowledge, this study is the first to couple non-invasive reflectance and fluorescence spectroscopic measurements on freshly harvested tissues with Monte Carlo computational modeling of timeresolved propagation of both excitation light and multi-fluorophore emission. For reflectance, quantitative agreement between simulation and experiment was achieved to better than 11%. Fluorescence data and simulations were used to extract the ratio of the absorption coefficients of constituent fluorophores for each measured AC tissue sample. This ratio could be used to monitor relative changes in concentration of the constituent fluorophores over time. The samples studied possessed the complexity and variability not found in artificial tissue-simulating phantoms and serve as a model for future optical molecular sensing studies on tissue engineered constructs intended for use in human therapeutics. An optical technique that could non-invasively and quantitatively assess soft tissue composition or physiologic status would represent a significant advance in tissue engineering. Moreover, the general approach described here for optical characterization should be broadly applicable to quantitative, non-invasive molecular sensing applications in complex, three-dimensional biological tissues.

Original languageEnglish (US)
Pages (from-to)6157-6171
Number of pages15
JournalOptics Express
Volume14
Issue number13
DOIs
StatePublished - Jun 26 2006
Externally publishedYes

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