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Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography

DOI: 10.1364/OE.418448 DOI Help

Authors: Nghia T. Vo (Diamond Light Source) , Robert C. Atwood (Diamond Light Source) , Michael Drakopoulos (National Synchrotron Light Source II) , Thomas Connolley (Diamond Light Source)
Co-authored by industrial partner: No

Type: Journal Paper
Journal: Optics Express , VOL 29

State: Published (Approved)
Published: May 2021
Diamond Proposal Number(s): 22109 , 24740 , 14033 , 19526 , 21999

Open Access Open Access

Abstract: Parallel-beam tomography systems at synchrotron facilities have limited field of view (FOV) determined by the available beam size and detector system coverage. Scanning the full size of samples bigger than the FOV requires various data acquisition schemes such as grid scan, 360-degree scan with offset center-of-rotation (COR), helical scan, or combinations of these schemes. Though straightforward to implement, these scanning techniques have not often been used due to the lack of software and methods to process such types of data in an easy and automated fashion. The ease of use and automation is critical at synchrotron facilities where using visual inspection in data processing steps such as image stitching, COR determination, or helical data conversion is impractical due to the large size of datasets. Here, we provide methods and their implementations in a Python package, named Algotom, for not only processing such data types but also with the highest quality possible. The efficiency and ease of use of these tools can help to extend applications of parallel-beam tomography systems.

Subject Areas: Information and Communication Technology

Instruments: I12-JEEP: Joint Engineering, Environmental and Processing

Added On: 02/06/2021 13:18


Discipline Tags:

Information & Communication Technologies Computing & software technologies Data processing

Technical Tags:

Imaging Tomography