Mueller matrix imaging of human colon tissue for cancer diagnostics: how Monte Carlo modeling can help in the interpretation of experimental data.
Colon samples with both healthy and cancerous regions have been imaged in diffuse light and backscattering geometry by using a Mueller imaging polarimeter. The tumoral parts at the early stage of cancer are found to be less depolarizing than the healthy ones. This trend clearly shows that polarimetric imaging may provide useful contrasts for optical biopsy. Moreover, both types of tissues are less depolarizing when the incident polarization is linear rather than circular. However, to really optimize an optical biopsy technique based on polarimetric imaging a realistic model is needed for polarized light scattering by tissues. Our approach to this goal is based on numerical Monte-Carlo simulations of polarized light propagation in biological tissues modeled as suspensions of monodisperse spherical scatterers representing the cell nuclei. The numerical simulations were validated by comparison with measurements on aqueous polystyrene sphere suspensions, which are commonly used as tissue phantoms. Such systems exhibit lower depolarization for incident linear polarization in the Rayleigh scattering regime, i.e. when the sphere diameters are smaller than the wavelength, which is obviously not the case for cell nuclei. In contrast, our results show that this behaviour can also be seen for "large" scatterers provided the optical index contrast between the spheres and the surrounding medium is small enough, as it is likely to be the case in biological tissues.
Antonelli
Maria-Rosaria
MR
LPICM, Ecole polytechnique, CNRS, Palaiseau 91128, France.
Pierangelo
Angelo
A
Novikova
Tatiana
T
Validire
Pierre
P
Benali
Abdelali
A
Gayet
Brice
B
De Martino
Antonello
A
D003106:Colon / Q000473:pathology*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008859:Microscopy, Polarization / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000473:pathology*; D009391:Nephelometry and Turbidimetry / Q000379:methods*; D012031:Refractometry / Q000379:methods*