2/5/2025 | 11:00 AM - 12:20 PM | Regency A

Physics-guided AI/ML models for ptychographic image reconstruction

Abstract

While the advances in synchrotron radiation sources, together with the development of focusing optics and detectors, allow nanoscale ptychographic imaging of materials and biological specimens, the corresponding experiments can yield terabyte-scale volumes of data with heavy computational demands and long data acquisition times. In this talk, I will introduce how AI/ML models can be integrated into ptychographic reconstruction tasks to reduce probe overlap requirements, thereby significantly decreasing both data acquisition and computational costs. Specifically, I will present two methods: a scalable ptychographic reconstruction technique with uncertainty estimates, and an image reconstruction method that uses a score-based diffusion model guided by ptychographic imaging physics. Our experimental evaluations show that these methods consistently yield high-quality image reconstructions, even with extremely low probe overlaps.