Dynamic Compressed Sensing for Real-Time Tomographic Reconstruction

PI Robert Hovden, University of Michigan
Co-PI Yi Jiang, Argonne National Laboratory
Huihuo Zheng, Argonne National Laboratory
Hovden ADSP 2021
Project Summary

 

With Argonne’s leadership in computing resources, we plan to continue conducting comprehensive simulations for real time electron tomography and develop reconstruction methods for through-focal tomography. We will understand the robustness of our algorithms, establish optimal sampling strategies, and validate convergence across parameter spac

Project Description

Using electron and X-ray tomography to perform 3D characterization of materials at the nano- and mesoscale is important to the development of a wide range of applications, including solar cells and semiconductor devices. To overcome experimental limitations and improve image quality in materials characterization research, researchers are leveraging recent advancements in tomographic reconstruction algorithms, such as compressed sensing methods, to provide superior 3D resolution. In the first year of this ADSP project, researchers developed a dynamic tomography framework that uses compressed sensing algorithms to perform in-situ reconstruction while new data is being collected. In year two, the team will continue to conduct comprehensive simulations for real-time electron tomography and develop reconstruction methods for through-focal tomography, an approach that enhances resolution by combining images captured at different levels of focus. They will experimentally demonstrate the reconstruction workflow and methods on commercial scanning transmission electron microscopes and the ptychographic tomography instruments at the APS. By integrating their tool with an open-source 3D visualization and tomography software package, the team’s techniques will be accessible to a wide range of researchers and enable new material characterizations in academia and industry.

Project Type
Allocations