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Self-learning GAN based synthetic CT generation: unlocking CBCT-based adaptive radiotherapy.

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Artificial intelligence generates synthetic CT (sCT) images from CBCT scans, improving image quality and dosimetric accuracy for adaptive radiotherapy across multiple cancer sites. This AI-driven approach enhances CBCT

Keywords:
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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Artificial Intelligence in Medicine

Background:

  • Cone-beam CT (CBCT) is used for patient positioning in radiotherapy.
  • Current CBCT image quality limits its full potential for adaptive radiotherapy.
  • Developing synthetic CT (sCT) from CBCT can improve treatment planning and delivery.

Purpose of the Study:

  • To clinically evaluate AI-generated synthetic CT (sCT) images from multi-center CBCT scans.
  • To assess the feasibility of using sCT for adaptive radiotherapy in various cancer types.
  • To leverage CBCT data for enhanced radiotherapy workflows.

Main Methods:

  • AI-based sCT models using cycleGAN architecture were developed for multiple anatomical sites.
  • 51 patients with head-and-neck, lung, pelvic, or breast cancer were included from two institutions.
  • Image accuracy (MAE, SSIM, PSNR) and dosimetric evaluation (gamma passing rates, dose-volume metrics) were performed.

Main Results:

  • The AI model significantly improved image quality: MAE decreased, SSIM and PSNR increased.
  • Dosimetric comparisons showed strong agreement between sCT and planning CT (pCT).
  • Median gamma passing rates reached 100% for clinical criteria across all sites.

Conclusions:

  • AI-generated sCT from CBCT is feasible for multiple anatomical sites.
  • The method provides consistent image quality improvements and reliable dosimetric accuracy.
  • This approach supports the clinical use of CBCT for adaptive radiotherapy.