ADVANCING DEEP TISSUE IMAGING
Visualizing cellular interactions and dynamic events in their native environments is essential for deepening our understanding of complex biological processes. However, achieving cellular resolution in deep imaging of turbid biological systems and live animals presents significant challenges. We aim to overcome these obstacles by developing advanced imaging systems that integrate quantitative phase microscopy, multiphoton microscopy, adaptive optics, and AI/ML. Collaborating with experts across various fields, including tissue engineering, neuroscience, cancer, immunology, and embryology, we demonstrate our imaging capabilities while addressing specific biological questions and providing tailored engineering solutions.
QUANTITATIVE IMAGING FOR LIVING ORGANOIDS WITH SPECIFICITY
Quantitative phase imaging (QPI) enables nanoscale-sensitivity, high-resolution, label-free visualization of biological samples by measuring optical path length differences in cellular structures. QPI provides insights into intrinsic cellular properties such as dry mass density and refractive index, allowing for monitoring dynamic processes, assessment of treatment responses, and detection of subtle changes related to disease progression. Combined with AI/ML, we can restore confocal resolution and biochemical specificity to label-free turbid living samples.
AI-DRIVEN SOLUTIONS
AI-driven solutions are transforming the way we approach complex biological questions. By harnessing the power of advanced imaging techniques and AI algorithms, we can analyze vast datasets, detect subtle patterns, and predict outcomes with unprecedented accuracy.
SOLVING THE INVERSE PROBLEM
Interpreting cellular and tissue images can be complex, as the image formation is influenced by both the object and the system’s transfer function, resulting in a distorted representation with limited information. Solving the inverse scattering problem enables the extraction of intrinsic object characteristics, such as refractive index (RI) and higher-order susceptibility distributions.
