Advances in vectorial polarization-resolved imaging are bringing new capabilities to applications ranging from fundamental physics through to clinical diagnosis. Imaging polarimetry requires determination of the Mueller matrix (MM) at every point, providing a complete description of an object’s vectorial properties. Despite forming a comprehensive representation, the MM does not usually provide easily interpretable information about the object’s internal structure. Certain simpler vectorial metrics are derived from subsets of the MM elements. These metrics permit extraction of signatures that provide direct indicators of hidden optical properties of complex systems, while featuring an intriguing asymmetry about what information can or cannot be inferred via these metrics. We harness such characteristics to reveal the spin Hall effect of light, infer microscopic structure within laser-written photonic waveguides, and conduct rapid pathological diagnosis through analysis of healthy and cancerous tissue. This provides new insight for the broader usage of such asymmetric inferred vectorial information.
Mueller matrix polarimetry has been applied to assist the diagnosis of several different types of diseases. The improvement of imaging resolution using objective with high numerical aperture (NA) is important for traditional optical microscope. However, imaging using a high NA objective entails a problem, namely, the field of view (FOV) is smaller and imaging speed is slower. Our previous work found that when using Mueller matrix microscope to obtain the structural features of tissue samples, some information of anisotropic structures, such as the density and orientation distribution of fibers can be revealed by polarization parameters images with relatively low resolution. In this study, we use objectives with different numerical aperture to measure the microscopic Mueller matrix of human healthy breast duct tissues and ductal carcinoma in situ (DCIS) tissues, which have distinct typical fibrous structures. Then a group of image texture feature parameters of Mueller matrix derived parameters images under high and low imaging resolutions are quantitatively compared. The results demonstrate that with the decline of imaging resolution, the fibers density information contained in the texture features of linear retardance δ parameter image are preserved well. While for the azimuthal orientation parameter θ which is closely related to the spatial location, the high imaging resolution to obtain quantitative structural information is still needed. The study provides an important criterion to decide which information of fibrous structures can be extracted accurately using transmission Mueller matrix microscope with low numerical aperture objectives to assist diagnose clinically such as breast ductal carcinoma.
Crohn's disease (CD) and gastrointestinal luminal tuberculosis (ITB) are two kinds of similar inflammatory bowel diseases, whose incidences are growing rapidly worldwide. Due to the lack of a general gold standard to distinguish between CD and ITB samples, misdiagnosis often occurs in clinical detections, leading to inappropriate treatments and side-effects. The characteristic features of both CD and ITB tissues include tuberculosis and surrounding fibrous structures, which can be quantitatively evaluated by polarimetric techniques. In this study, we apply the transmission Mueller matrix microscope developed in our previous study on the CD and ITB tissue samples to attain their 2D Mueller matrix images. We calculate the Mueller matrix polar decomposition and transformation parameters, which can provide information about the location, density and distribution behavior of the tuberculosis areas surrounded by fibrous structures. In order to evaluate the different distribution behaviors of the fibrous structures quantitatively, we analyzed the retardance related Mueller matrix derived parameters images, which show different features between the CD and ITB tissues, using the Tamura images processing method (TIPM). The preliminary results show that the TIPM analysis of the retardance related parameters can provide some quantitative parameters to describe the different textures of fibers in the CD and ITB tissues. Moreover, we use the machine learning method based on Mueller matrix derived parameters to distinguish between CD and ITB tissues. It is demonstrated that the Mueller matrix derived parameters combined with machine learning methods can be helpful for clinical diagnosis.
Breast cancer is a serious threat to women worldwide due to its high incidence and mortality. The early detection is very crucial for the treatment of breast cancer. Currently, for breast cancer patients, mammography and stereotactic needle biopsy followed by time-consuming pathological observations are the primary diagnostic approaches. In our previous study, it was found that the characteristic features of breast carcinoma tissues often include fibrous structures induced by inflammatory reactions, which can be quantitatively evaluated by polarimetric techniques. In this study, we further measure the transmission Mueller matrix microscopic images of 30 breast ductal tissue samples at different progression stages. We calculate the Mueller matrix derived parameters, which can provide the quantitative information on the location, density and distribution behavior of the fibrous structures in the tissues. To evaluate the distribution behavior of fibrous structures more quantitatively and precisely, we also analyze the parameters δ and θ using the gray level cooccurrence matrix (GLCM) analyzing method. The results demonstrate that, the GLCM features Contrast, Energy, Correlation and Homogeneity of δ and θ can be used to describe different textures of fibers distributions among the healthy, ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) tissue samples, whereas the parameters of unpolarized light intensity images show no prominent differences. The Mueller matrix derived parameters combined with image analyzing methods can be used for label-free detecting and quantitative staging of breast carcinoma tissues, which can be helpful for clinical diagnosis.
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