Three-dimensional (3D) fluorescence microscopy is limited by slow scanning speeds and susceptibility to photobleaching, while faster volumetric imaging modalities frequently impose prohibitive costs or significant resolution tradeoffs. In this seminar, we present an easily adaptable solution for snapshot 3D microscopy via the joint optimization of an axial-encoding Point Spread Function (PSF) and a deep learning-based reconstruction algorithm.
We will explore the problem domain and challenges, as well as the building blocks of our solution: designing, collecting, and preprocessing a large-scale 3D microscopy dataset, physics-aware differentiable imaging model, imaging-plane selection module, and model architecture.
Our method enables single-shot 3D volumetric reconstruction using an engineered depth-encoding PSF, allowing 2D-to-3D dynamic imaging restoration. We will review results in simulated and experimental settings of static and live samples.
Danielle Sapir is an M.Sc. candidate under the supervision of Prof. Yoav Shechtman.