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Optimizing laboratory X-ray diffraction contrast tomography: Effects of detector binning.

Xinbo Ni1, Yiping Xia2, Haixing Fang3

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|May 2, 2026
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Summary

Detector binning in laboratory-based diffraction contrast tomography (LabDCT) improves efficiency but reduces accuracy. Unbinned data offers better grain indexing and boundary accuracy, though deep learning can mitigate binning-induced losses.

Keywords:
3D grain mappingBinning modeContrast tomographyDiffractionX-ray diffraction

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Area of Science:

  • Materials Science
  • Crystallography
  • Imaging Techniques

Background:

  • Laboratory-based diffraction contrast tomography (LabDCT) utilizes pixel binning to enhance acquisition efficiency.
  • The effect of detector binning on LabDCT reconstruction accuracy is not well understood.

Purpose of the Study:

  • To systematically evaluate the impact of 2x2 detector binning on LabDCT reconstruction accuracy.
  • To compare binned and unbinned LabDCT data using synchrotron-based DCT (SRDCT) as a reference.

Main Methods:

  • Acquisition of LabDCT datasets with and without 2x2 detector binning.
  • Comparison of reconstruction accuracy, grain indexing rates, and grain boundary positions.
  • Application of a deep-learning spot-segmentation model (Mask R-CNN) to binned data.

Main Results:

  • Unbinned (bin1) LabDCT data showed higher grain indexing rates and improved grain boundary accuracy compared to binned (bin2) data.
  • Improvements were most notable for small grains near the sample surface.
  • Deep learning-based spot segmentation significantly enhanced reconstructions from binned images, reducing the performance gap.

Conclusions:

  • While detector binning in LabDCT improves efficiency, it compromises reconstruction accuracy, especially for fine details.
  • Deep learning models offer a promising approach to recover lost information and improve reconstructions from binned LabDCT data.
  • Guidelines for optimizing LabDCT acquisition and reconstruction parameters are proposed.