No abstract

In the framework of further development of a unified computational tool for the needs of biomedical optics, we introduce an electric field Monte Carlo (MC) model for simulation of backscattering of coherent linearly polarized light from a turbid tissue-like scattering medium with a rough surface. We consider the laser speckle patterns formation and the role of surface roughness in the depolarization of linearly polarized light backscattered from the medium. The mutual phase shifts due to the photons’ pathlength difference within the medium and due to reflection/refraction on the rough surface of the medium are taken into account. The validation of the model includes the creation of the phantoms of various roughness and optical properties, measurements of co- and cross-polarized components of the backscattered/reflected light, its analysis and extensive computer modeling accelerated by parallel computing on the NVIDIA graphics processing units using compute unified device architecture (CUDA). The analysis of the spatial intensity distribution is based on second-order statistics that shows a strong correlation with the surface roughness, both with the results of modeling and experiment. The results of modeling show a good agreement with the results of experimental measurements on phantoms mimicking human skin. The developed MC approach can be used for the direct simulation of light scattered by the turbid scattering medium with various roughness of the surface.

Skin cancer is the most common cancer in the Western world. In order to accurately detect the disease, especially malignant melanoma-the most fatal form of skin cancer-at an early stage when the prognosis is excellent, there is an urgent need to develop noninvasive early detection methods. We believe that polarization speckle patterns, defined as a spatial distribution of depolarization ratio of traditional speckle patterns, can be an important tool for skin cancer detection. To demonstrate our technique, we conduct a large in vivo clinical study of 214 skin lesions, and show that statistical moments of the polarization speckle pattern could differentiate different types of skin lesions, including three common types of skin cancers, malignant melanoma, squamous cell carcinoma, basal cell carcinoma, and two benign lesions, melanocytic nevus and seborrheic keratoses. In particular, the fourth order moment achieves better or similar sensitivity and specificity than many well-known and accepted optical techniques used to differentiate melanoma and seborrheic keratosis.