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Determination of Crystal Structures01:29

Determination of Crystal Structures

In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...

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Development of a customizable model for spectral photon-counting detector CT.

Mridul Bhattarai1,2,3, Raj Kumar Panta2,3, W Paul Segars1,2,3

  • 1Center for Virtual Imaging Trials, Department of Radiology, Duke University, Durham, North Carolina, USA.

Medical Physics
|December 8, 2025
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Summary

A new simulation framework, DukeCounter, models photon-counting detector CT (PCD-CT) systems, enabling evaluation of various detector designs and optimizing medical imaging. This reduces the need for physical experiments, saving time and resources.

Keywords:
CT simulationphoton counting detector CTspatio‐energetic detector response

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Area of Science:

  • Medical Imaging Physics
  • Computational Modeling
  • Detector Technology

Background:

  • Photon-counting detector CT (PCD-CT) offers advanced spatial and spectral imaging.
  • Evaluating PCD-CT performance requires a comprehensive framework for task-generic and task-specific image quality.

Purpose of the Study:

  • Develop and validate a customizable, physics-informed simulation framework for spatio-energetic detector responses in PCD-CT.
  • Integrate the framework into a virtual imaging system.
  • Demonstrate its application in clinical imaging tasks.

Main Methods:

  • Developed DukeCounter, a customizable simulation model for PCD-CT systems.
  • Modeled photon transport, crosstalk, and charge sharing using Monte Carlo and analytical methods.
  • Generated spatio-energetic detector responses for various PCD materials (CdTe, CZT, GaAs, Si).
  • Integrated responses into a CT simulator to create virtual PCD-CT scanners.
  • Validated the framework against experimental data and conducted pilot studies on image quality, COPD biomarker extraction, and liver lesion detection.

Main Results:

  • Simulated charge cloud size varied with energy and PCD material.
  • Detector responses differed based on PCD material, design, and energy thresholds.
  • Strong agreement between simulated and real ACR images was observed.
  • Task-generic and task-specific image qualities were influenced by PCD parameters.
  • GaAs-based systems showed higher noise and poorer performance in biomarker quantification and lesion detection.

Conclusions:

  • A customizable, modular simulation framework (DukeCounter) was successfully developed for modeling PCD-CT systems.
  • The framework enables systematic evaluation and optimization of PCD-CT designs.
  • This approach supports translational research by reducing costs, time, and radiation exposure associated with physical experiments.