|About this Abstract
||MS&T21: Materials Science & Technology
||AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
||Now On-Demand Only: Non-iterative Deep Learning for High-fidelity Microscopic Tomography
||Singanallur Venkatakrishnan, Amir Koushyar Ziabari, Jacob Hinkle, Micheal Kirka , Jeffrey Warren, Hassina Bilheux, Vincent Paquit, Ryan DeHoff
|On-Site Speaker (Planned)
Microscopic computer tomography (CT) is a ubiquitous technique for 3D non-destructive characterization of samples from the nano-meter to micron length scales. However, obtaining high quality 3D reconstructions can be challenging because of the low signal-to-noise ratio, the sparse angular sampling dictated by realistic experiment times, and the complex physics associated with the measurements.
In this talk, we will present the development of non-iterative deep-learning-based reconstruction algorithms for microscopic CT data. We will discuss how different network architectures and sources of limited training data (reference samples, single step from a time-resolved scan, and computer-aided design models) can be leveraged to obtain high quality reconstructions from X-ray and neutron CT instruments.
These improvements in image quality offered by the deep-learning approach leads to a more efficient use of instrument time enabling faster scans and higher spatio-temporal resolution for time-resolved experiments.