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SU-E-J-41: Fluoroscopy Based Adaptive Setup Approach for Thoracic Cancer IGRT.

T Chen1,2,1,1,1,3, S Qin1,2,1,1,1,3, S Jabbour1,2,1,1,1,3

  • 1Cancer Institute of New Jersey, New Brunswick, NJ.

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Summary
This summary is machine-generated.

This study introduces a machine learning method using fluoroscopy images to precisely measure respiratory motion variations. This technique quantifies patient setup deviations for improved thoracic cancer image-guided radiation therapy (IGRT).

Keywords:
CancerFluoroscopyImage guided radiation therapyMachine learningManifoldsMedical image qualityMedical imagingMedical treatment planningRandom noiseTissues

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Radiation Oncology

Background:

  • Accurate patient positioning is crucial in thoracic cancer image-guided radiation therapy (IGRT).
  • Respiratory motion during treatment can lead to significant setup deviations, impacting treatment efficacy and normal tissue toxicity.
  • Real-time adaptive adjustments require precise quantification of motion and positional variations.

Purpose of the Study:

  • To develop a fluoroscopy imaging-based approach for quantifying respiratory motion magnitude and phase variations.
  • To determine online patient setup deviations in thoracic cancer IGRT.
  • To enable real-time adaptive patient position adjustments.

Main Methods:

  • A numerical phantom with simulated respiratory motion (varying frequency, amplitude, offset, phase shift) was used.
  • White noise (SNR=5) was added to simulate clinical image quality.
  • A manifold-based machine learning technique constructed a respiratory motion model, and MAP solutions quantified phase and position shifts.

Main Results:

  • The approach successfully detected variations in motion patterns between image sets.
  • It demonstrated insensitivity to frequency changes and image noise up to SNR=5.
  • The method effectively captured and quantified changes in motion amplitude, position shift, and phase shift.

Conclusions:

  • An effective mathematical approach quantifies motion differences between pre-treatment and planning images using machine learning on fluoroscopy.
  • Application during online patient setup allows separation and adjustment of positioning deviations from respiratory motion.
  • This facilitates minimized normal tissue toxicity in gated IGRT.