Reflectance-based determination of optical properties in highly attenuating tissue.
Accurate data on in vivo tissue optical properties in the ultraviolet A (UVA) to visible (VIS) range are needed to elucidate light propagation effects and to aid in identifying safe exposure limits for biomedical optical spectroscopy. We have performed a preliminary study toward the development of a diffuse reflectance system with maximum fiber separation distance of less than 2.5 mm. The ultimate objective is to perform endoscopic measurement of optical properties in the UVA to VIS. Optical property sets with uniformly and randomly distributed values were developed within the range of interest: absorption coefficients from 1 to 25 cm(-1) and reduced scattering coefficients from 5 to 25 cm(-1). Reflectance datasets were generated by direct measurement of Intralipid-dye tissue phantoms at lambda=675 nm and Monte Carlo simulation of light propagation. Multivariate calibration models were generated using feed-forward artificial neural network or partial least squares algorithms. Models were calibrated and evaluated using simulated or measured reflectance datasets. The most accurate models developed-those based on a neural network and uniform optical property intervals-were able to determine absorption and reduced scattering coefficients with root mean square errors of +/-2 and +/-3 cm(-1), respectively. Measurements of ex vivo bovine liver at 543 and 633 nm were within 5 to 30% of values reported in the literature. While our technique for determination of optical properties appears feasible and moderately accurate, enhanced accuracy may be achieved through modification of the experimental system and processing algorithms.
Pfefer
T Joshua
TJ
US Food and Drug Administration, Center for Devices and Radiological Health, Rockville, Maryland 20857, USA. tjp@cdrh.fda.gov
Matchette
L Stephanie
LS
Bennett
Carrie L
CL
Gall
Jessica A
JA
Wilke
Joy N
JN
Durkin
Anthony J
AJ
Ediger
Marwood N
MN
D000465:Algorithms; D000818:Animals; D002138:Calibration / Q000592:standards; D002417:Cattle; D004724:Endoscopy / Q000379:methods* / Q000592:standards; D007089:Image Enhancement / Q000379:methods / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D066298:In Vitro Techniques; D008027:Light; D008099:Liver / Q000502:physiology*; D008954:Models, Biological; D016571:Neural Networks, Computer; D019047:Phantoms, Imaging; D012542:Scattering, Radiation; D013057:Spectrum Analysis / Q000379:methods; D041622:Tomography, Optical / Q000295:instrumentation / Q000379:methods* / Q000592:standards