Abstract
A new and noncontact approach of multispectral reflectance imaging has been developed to inversely determine the absorption coefficient of μ a , the scattering coefficient of μs and the anisotropy factor g of a turbid target from one measured reflectance image. The incident beam was profiled with a diffuse reflectance standard for deriving both measured and calculated reflectance images. A GPU implemented Monte Carlo code was developed to determine the parameters with a conjugate gradient descent algorithm and the existence of unique solutions was shown. We noninvasively determined embedded region thickness in heterogeneous targets and estimated in vivo optical parameters of nevi from 4 patients between 500 and 950nm for melanoma diagnosis to demonstrate the potentials of quantitative reflectance imaging.
Highlights
Quantitative characterization of turbidity attracts intense research interests for tasks ranging from materials analysis to disease diagnosis [1, 2]
We present a new approach of reflectance imaging that combines multispectral image acquisition, radiative transfer (RT) theory based Monte Carlo (MC) simulations of light transport [20,21,22,23], graphics processing unit (GPU) implementation of MC simulation [24, 25] and a conjugate gradient [26, 27] based inverse algorithm to retrieve a set of target parameters P
We briefly describe the logic flow of the individual photon tracking MC (iMC) code here for simulation of reflectance imaging with details of the algorithm and validation results given elsewhere [20, 24, 28, 29]
Summary
Quantitative characterization of turbidity attracts intense research interests for tasks ranging from materials analysis to disease diagnosis [1, 2]. Most approaches of reflectance imaging realized to date, rely on pattern analysis to characterize targets for recognition and classification [3,4,5,6,7,8,9] While these methods require no modeling of light-matter interaction, their applications depend sensitively on configurations of illumination and detection. Improvement with the radiative transfer (RT) theory has been reported for inverse determination of μa, scattering coefficient μs and anisotropy factor g of homogeneous turbid samples [18, 19] Despite these advances, optical model based reflectance imaging is far from the point for translation into practical systems to characterize turbidity according to the RT theory [1]. A new method should have the capacity to determine spatial distribution of optical and size parameters in heterogeneous turbid targets, which is necessary in applications including diagnosis and delineate normal and cancerous tissues
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