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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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Application of machine learning classifiers to X-ray diffraction imaging with medically relevant phantoms.

Stefan Stryker1, Anuj J Kapadia1,2, Joel A Greenberg1,3

  • 1Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA.

Medical Physics
|November 20, 2021
PubMed
Summary

Machine learning classifiers significantly improved X-ray diffraction (XRD) image analysis for medical applications, outperforming traditional methods. These advanced algorithms enhance material classification accuracy, especially in complex regions, paving the way for better diagnostics.

Keywords:
X-ray contrastX-ray diffraction imagingcoded aperturemachine learningmedical phantomsmultimodal imaging

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Area of Science:

  • Medical Imaging
  • Materials Science
  • Computational Biology

Background:

  • X-ray diffraction (XRD) imaging offers rich data for disease analysis.
  • Developing advanced algorithms is crucial for diagnostic applications of XRD.
  • This study compares rules-based and machine learning (ML) classifiers for XRD image analysis.

Purpose of the Study:

  • To implement and compare rules-based and ML classifiers on XRD images.
  • To evaluate the potential for improved classification performance using these algorithms.
  • To explore the diagnostic capabilities of XRD imaging in medical contexts.

Main Methods:

  • Utilized medically relevant phantoms (water/PLA) with varying complexity.
  • Acquired co-registered X-ray transmission and diffraction images.
  • Compared cross-correlation, linear least-squares, support vector machines, and shallow neural networks.

Main Results:

  • Shallow neural networks achieved the highest AUC (0.999) and accuracy (98.94%).
  • ML classifiers outperformed rules-based methods, especially in regions with mixed materials.
  • XRD imaging with ML significantly surpassed transmission imaging alone (AUC 0.773).

Conclusions:

  • ML-based classifiers demonstrate superior performance over rules-based methods for XRD image analysis.
  • Improved spatially resolved classification highlights the potential for enhanced material analysis.
  • These findings support the use of XRD imaging and ML for research, industrial, and clinical applications.