|About this Abstract
||MS&T21: Materials Science & Technology
||AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
||Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
||Hamidreza T-Sarraf, Sridhar Niverty, Nikhilesh Chawla
|On-Site Speaker (Planned)
Time-dependent x-ray tomography (4D) is an excellent approach to understand material behavior. The quality of the x-ray projections is proportional to the x-ray exposure time. Also, image modalities such as phase-contrast and diffraction-contrast can be used to highlight different microstructural features. These factors extend the scan time and limit the number of scan iterations for time-evolved tomography experiments. Moreover, image processing and segmentation are extremely time-intensive for 4D tomographic data. Thus, there is a need to establish a robust workflow and algorithms that can render time-dependent x-ray datasets accurately and efficiently. In this study, we discuss the utility and efficiency of different Deep Convolutional Neural Network (DCNN) architectures and Generative Adversarial Network (GAN) algorithms for quality enhancement and automated segmentation of x-ray tomography datasets obtained by different modalities. These developments demonstrate the ability to drastically reduce x-ray data acquisition times, thereby opening the window for efficient 4D experiments.