Automatic synchrotron tomographic alignment schemes based on genetic algorithms and human-in-the-loop software

Area of Science:

• Physics
• Materials Science
• Imaging Technology

Background:

• Synchrotron-based tomography imaging is advancing, requiring high spatial and temporal resolutions.
• Complex experimental setups with small probes and multi-dimensional scanning pose alignment challenges.
• Current post-processing alignment methods are time-consuming and cannot fully correct errors.

Purpose of the Study:

• To develop an automated alignment scheme for X-ray tomography systems.
• To reduce human effort and beam time spent on experimental setup.
• To improve the efficiency and accuracy of data acquisition.

Main Methods:

• A software-based approach combining genetic algorithms and human intelligence was developed.
• The method focuses on automatic alignment of the rotational axis and sample.
• Alignment is performed both before and during data acquisition.

Main Results:

• The automated scheme achieved excellent sub-pixel alignment efficiency.
• The alignment process was completed in a short amount of time.
• The approach demonstrated effectiveness for both rotational axis and sample alignment.

Conclusions:

• The developed automated alignment scheme significantly enhances X-ray tomography experiments.
• This method offers a time-efficient solution for complex alignment tasks.
• The approach has strong potential for integration into future scanning tomography data acquisition systems.