Phase Contrast

Phase contrast imaging aims to visualize and quantify variations in refractive index within a sample, providing label-free information about its structure and composition. Current research emphasizes developing faster and more robust phase retrieval methods, employing techniques like deep learning (including diffusion models and neural networks), ptychography, and advanced microscopy configurations (e.g., multi-core fiber endoscopes). These advancements are improving the speed, resolution, and accessibility of phase contrast imaging, with significant implications for biomedical imaging (e.g., cell segmentation, vascular analysis), materials science, and other fields requiring high-resolution, non-invasive visualization.