Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.

Area of Science:

• Advanced materials science and imaging techniques.
• Physics and data science intersection.

Background:

• Synchrotron tomography experiments are evolving towards complex, dynamic, and cross-scale characterizations.
• New light sources and instrumentation generate unprecedented data volumes, creating significant processing challenges.

Purpose of the Study:

• To review the application of deep learning in synchrotron tomography data processing.
• To explore the potential migration of deep learning methods from related fields.
• To discuss future challenges, opportunities, and outlook for deep learning in this domain.

Main Methods:

• Review of recent advances in deep learning for synchrotron tomography data processing.
• Analysis of deep learning applications in medical and electron tomography.
• Discussion of future deep learning strategies, including curated models and intelligent scheduling.

Main Results:

• Deep learning demonstrates high accuracy and efficiency for synchrotron tomography data processing.
• Methods from medical and electron tomography can be adapted for synchrotron applications.
• Future directions involve tailored deep learning models and intelligent data management.

Conclusions:

• Deep learning is crucial for managing big data challenges in future synchrotron beamlines.
• Cross-disciplinary application of deep learning can accelerate advancements in synchrotron tomography.
• Intelligent solutions integrating deep learning methods are essential for future research.