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Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
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Fast internal marker tracking algorithm for onboard MV and kV imaging systems.

W Mao1, R D Wiersma, L Xing

  • 1Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305-5847, USA.

Medical Physics
|June 20, 2008
PubMed
Summary
This summary is machine-generated.

This study presents an automated algorithm for detecting implanted metallic markers in kV and MV images, crucial for accurate radiation therapy targeting. The algorithm achieves 100% detection success in phantom studies and near real-time speeds for precise tumor tracking.

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Area of Science:

  • Medical Physics
  • Image Processing
  • Radiation Oncology

Background:

  • Intrafraction organ motion complicates precise radiation delivery in techniques like intensity-modulated radiation therapy (IMRT).
  • Real-time tracking of internal radio-opaque markers is essential for accurate beam targeting during radiation therapy.
  • Detecting markers in megavoltage (MV) images is challenging due to low contrast compared to kilovoltage (kV) imaging.

Purpose of the Study:

  • To develop and validate a fully automated algorithm for detecting implanted metallic markers in both kV and MV imaging modalities.
  • To improve the accuracy and reliability of target localization during radiation therapy by enabling real-time marker tracking.
  • To overcome the limitations of low contrast in MV imaging for marker detection.

Main Methods:

  • An automated algorithm was developed to detect spherical and cylindrical metallic markers using prior CT information to define search spaces.
  • The algorithm operates without manual region-of-interest (ROI) selection, allowing for automated marker detection.
  • The system was tested using phantom studies and real patient MV image data, evaluating detection rates and operational speed.

Main Results:

  • The algorithm demonstrated 100% detection success rates for both kV and MV image data in phantom studies.
  • Successful detection of all implanted markers was achieved in real patient MV image data.
  • Near real-time performance of approximately 10 frames/sec was accomplished for detecting multiple markers on a standard PC.

Conclusions:

  • The developed automated algorithm reliably detects implanted metallic markers in kV and MV images, enhancing precision in radiation therapy.
  • This technology offers a robust solution for real-time target localization, mitigating issues caused by intrafraction motion.
  • The algorithm's efficiency and accuracy support its clinical application for improved patient treatment outcomes.