Maximizing 3D X-ray Microscopy Data Fidelity and Throughput with Novel Deep Learning Assisted Reconstruction Algorithms — ASN Events

Maximizing 3D X-ray Microscopy Data Fidelity and Throughput with Novel Deep Learning Assisted Reconstruction Algorithms — ASN Events

Maximizing 3D X-ray Microscopy Data Fidelity and Throughput with Novel Deep Learning Assisted Reconstruction Algorithms

Maximizing 3D X-ray Microscopy Data Fidelity and Throughput with Novel Deep Learning Assisted Reconstruction Algorithms (114381)

Bernd Schulz ¹ , Luna Zhang ² , Stephen T. Kelly ³ , Nathan Johnson ³ , Feng Lin Ng ²

Carl Zeiss Pty Ltd, Australia
Carl Zeiss Pte. Ltd, Singapore, SINGAPORE
ZEISS Research Microscopy Solutions, Dublin, CA

Authors contributing to this presentation.

Schulz, B

Bernd Schulz oversees the Materials Science portfolio at ZEISS Australia and New Zealand. He holds a PhD from UNSW Sydney and has over a decade experience as Materials Scientist within academic research and the manufacturing industry.

Zhang, L

T. Kelly, S.

Johnson, N

Ng, F