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Related Experiment Video

Updated: Jun 25, 2026

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
06:53

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm

Published on: July 23, 2020

Markerless gating for lung cancer radiotherapy based on machine learning techniques.

Tong Lin1, Ruijiang Li, Xiaoli Tang

  • 1Department of Radiation Oncology, University of California San Diego, La Jolla, CA 92093, USA.

Physics in Medicine and Biology
|February 21, 2009
PubMed
Summary

Respiratory gating for lung cancer radiotherapy can be improved using marker-less methods. Artificial neural networks combined with principal component analysis offer superior real-time performance for gating without implanted fiducial markers.

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Machine Learning

Background:

  • Respiratory gating is crucial for lung cancer radiotherapy to account for target motion.
  • Current methods rely on fiducial markers, posing risks like pneumothorax.
  • Marker-less gating is a promising alternative for safer clinical application.

Purpose of the Study:

  • To investigate and compare different machine learning techniques for marker-less respiratory gating in lung cancer radiotherapy.
  • To evaluate the performance of dimensionality reduction methods combined with classification algorithms.
  • To identify the optimal combination for real-time, accurate gating.

Main Methods:

  • Developed a framework modeling gating as a binary pattern classification problem.

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Last Updated: Jun 25, 2026

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
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  • Evaluated combinations of dimensionality reduction (PCA, nonlinear manifold learning) and classification (ANN, SVM).
  • Tested performance on ten fluoroscopic image sequences from nine lung cancer patients.

    • Main Results:

    • Principal Component Analysis (PCA) combined with Artificial Neural Networks (ANN) or Support Vector Machines (SVM) outperformed nonlinear manifold learning methods.
    • ANN with PCA demonstrated higher classification accuracy and recall rate compared to SVM.
    • Both ANN and SVM with PCA showed processing times suitable for real-time radiotherapy applications.

    Conclusions:

    • Artificial Neural Networks combined with Principal Component Analysis is the most effective method for real-time marker-less respiratory gating in lung cancer radiotherapy.
    • This approach offers a safer and potentially more accurate alternative to fiducial marker-based gating.
    • The findings support the clinical implementation of advanced machine learning for improved radiotherapy delivery.