About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
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Symposium
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AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
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Presentation Title |
Deep Learning-based Algorithms for X-ray Microtomography Analysis: Unravelling Challenges for 4D Experiments |
Author(s) |
Hamid Torbatisarraf, Nikhilesh Chawla |
On-Site Speaker (Planned) |
Nikhilesh Chawla |
Abstract Scope |
X-ray microtomography is an important 3D characterization technique due to its non-destructive nature which provides time-dependent (4D) information. However, image processing and segmentation of 4D tomographic data is extremely time intensive. Moreover, factors such as phase transformation or defect propagation during a time-evolved tomography experiment limits the scan time and/or number of scan iterations.
Thus, there is a need to establish a robust algorithm that can render time-dependent x-ray datasets accurately and efficiently. In this talk, we describe the application of Deep Convolutional Neural Network (DCNN) algorithms for X-ray image quality enhancement and segmentation. Using a modified Generative Adversarial Network (GAN) algorithm we provide a workflow to transform low quality x-ray tomography acquired by a fast scan to a high-quality dataset. Our results point to the ability to drastically reduce x-ray data acquisition times, thereby opening a window for efficient 4D experiments. |