Definite diagnosis of brain neoplasms has been a challenge of neurosurgery practice especially when open surgery is not feasible to obtain adequate tissue samples for pathological investigation (e.g., for deep-seated tumors or tumors of eloquent areas undergoing biopsies or recurrent cases in patients that are candidates for palliative care). This complexity is exacerbated due to tumor heterogeneity, particularly when relying on limited samples obtained through stereotactic biopsies, which increases the risk of misdiagnosis. Addressing this critical issue, we propose a novel, label-free diagnostic approach by combining the strengths of polarimetry and speckle pattern analysis. This research introduces an innovative method that integrates Mueller matrix polarimetry with dynamic laser speckle analysis, offering a promising avenue for precise differentiation of biomedical samples and tissues. We validate the effectiveness of our technique by calculating the Mueller matrix images of the two various subtypes of brain neoplasms, followed by speckle pattern analysis through both numerical and graphical statistical post-processing. The results indicate considerable discrimination between the tissues, highlighting the potential of this methodology as an easily implementable non-invasive quantitative tool for analyzing extensive cancerous specimens.
Monitoring pitting corrosion in real-time and non-destructively is a critical challenge in material science. This study introduces dynamic speckle analysis as an innovative approach to quantitatively track pitting corrosion in metallic structures. Pitting corrosion, a major cause of structural failure due to hole formation, is effectively visualized using this technique. Dynamic speckle patterns, generated by laser light scattering off the corroded surface, reveal both the activity and progression of corrosion. We employ a range of graphical and numerical statistical parameters for detailed analysis. Notable for its low cost, ease of implementation, and quasi-in-situ capabilities, our method offers a significant advancement in the study of corrosion. It holds great promise for broader applications in both natural and industrial settings, presenting a versatile tool for material degradation assessment.
KEYWORDS: Digital holography, Holograms, Holography, Microscopy, 3D image reconstruction, Optical signal processing, 3D image processing, Microscopes, Beam splitters, Visualization
Lateral in-homogeneities in lipid compositions cause microdomains formation and change in the physical properties of biological membranes. With the presence of cholesterol and mixed species of lipids, phospholipid membranes segregate into lateral domains of liquid-ordered and liquid-disordered phases. Coupling of two-dimensional intralayer phase separations and interlayer liquid-crystalline ordering in multicomponent membranes has been previously demonstrated. By the use of digital holographic microscopy (DHMicroscopy), we quantitatively analyzed the volumetric dynamical behavior of such membranes. The specimens are lipid mixtures composed of sphingomyelin, cholesterol, and unsaturated phospholipid, 1,2-dioleoyl-sn-glycero-3-phosphocholine. DHMicroscopy in a transmission mode is an effective tool for quantitative visualization of phase objects. By deriving the associated phase changes, three-dimensional information on the morphology variation of lipid stacks at arbitrary time scales is obtained. Moreover, the thickness distribution of the object at demanded axial planes can be obtained by numerical focusing. Our results show that the volume evolution of lipid domains follows approximately the same universal growth law of previously reported area evolution. However, the thickness of the domains does not alter significantly by time; therefore, the volume evolution is mostly attributed to the changes in area dynamics. These results might be useful in the field of membrane-based functional materials.
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