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Related Experiment Video

Updated: May 22, 2025

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
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Deep learning-based Fast Volumetric Image Generation for Image-guided Proton Radiotherapy.

Chih-Wei Chang1, Yang Lei1, Tonghe Wang2

  • 1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322.

IEEE Transactions on Radiation and Plasma Medical Sciences
|May 19, 2025
PubMed
Summary

This study introduces a deep learning framework for rapid 3D image reconstruction, improving lung cancer treatment precision with image-guided radiation therapy. Optimal kV projection angles were identified for accurate target localization in FLASH proton therapy.

Keywords:
4D CTDeep learningImage synthesisImage-guided radiation therapy

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Artificial Intelligence in Medicine

Background:

  • Image-guided radiation therapy (IGRT) precision is crucial for effective cancer treatment.
  • Fast imaging techniques are needed to improve IGRT, especially for lung cancer patients requiring gating.
  • FLASH radiotherapy offers potential benefits for organ-at-risk sparing without compromising tumor control.

Purpose of the Study:

  • To develop and validate a deep learning (DL)-based framework for rapid volumetric image reconstruction.
  • To enable accurate target localization for lung cancer patients undergoing image-guided radiation therapy.
  • To evaluate the framework's performance in the context of proton FLASH therapy.

Main Methods:

  • A four-module framework was developed: kV x-ray projection acquisition, DL-based volumetric image generation, image quality analysis, and proton water equivalent thickness (WET) evaluation.
  • Volumetric images were reconstructed using kV projection pairs from four different source angles.
  • Thirty lung cancer patient datasets with 4D CT scans were utilized for evaluation.

Main Results:

  • The optimal kV projection source angles for volumetric image reconstruction were identified as 135° and 225°.
  • The framework achieved patient-averaged performance metrics including mean absolute error of 75±22 HU, peak signal-to-noise ratio of 19±3.7 dB, structural similarity index measure of 0.938±0.044, and WET error of -1.3%±4.1%.
  • The developed framework demonstrated rapid volumetric image delivery capabilities.

Conclusions:

  • The proposed DL-based framework enables fast volumetric image reconstruction for precise target localization in lung cancer patients.
  • This technology has the potential to guide proton FLASH treatment delivery systems, enhancing therapeutic precision and safety.
  • The identified optimal kV projection angles contribute to improved image quality for advanced radiotherapy applications.