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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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  1. Home
  3. Daily Proton Dose Re-calculation On Deep-learning Corrected Cone-beam Computed Tomography Scans
  1. Home
  3. Daily Proton Dose Re-calculation On Deep-learning Corrected Cone-beam Computed Tomography Scans

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Daily proton dose re-calculation on deep-learning corrected cone-beam computed tomography scans

Casper Dueholm Vestergaard1, Ludvig Paul Muren1, Ulrik Vindelev Elstrøm2

  • 1Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.

Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology
|May 24, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A deep learning network generates synthetic CT images from CBCT for adaptive proton therapy. This method ensures accurate dose calculations and stable performance throughout prostate cancer treatment, supporting robust clinical applications.

Keywords:
Adaptive proton therapyCone-beam CTDeep-learningProstate cancerProton dose trendingSynthetic CT

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Accurate dose calculation is crucial for adaptive proton therapy.
  • Synthetic CT (sCT) generation from cone-beam CT (CBCT) needs stable performance across treatment fractions.

Purpose of the Study:

  • To evaluate a 3D deep learning (DL) network for sCT generation from CBCT in prostate cancer patients.
  • To assess the feasibility of DL-based sCT for adaptive proton therapy over the full treatment course.

Main Methods:

  • A 3D DL network was trained and tested using data from 25 prostate cancer patients.
  • Generated sCT images were compared to planning CT and repeat CT (reCT) for CT number accuracy, proton range, and dose distribution.
  • Evaluated CT number accuracy in ROIs (bladder, prostate, femoral heads), proton range accuracy, and dose trends in target coverage.

Main Results:

  • sCT images exhibited comparable image quality and anatomy preservation to planning CT.
  • Mean CT numbers in ROIs were comparable between sCT and CT.
  • The largest median proton range difference was 1.9 mm, with excellent target coverage (V95%≥99.6%) in dose calculations.

Conclusions:

  • The DL network successfully generated high-quality sCT images suitable for adaptive proton therapy.
  • sCT images demonstrated comparable CT numbers, proton range, and dose characteristics to fan-beam CT.
  • The DL network's robustness to intra-patient variations supports its use in adaptive radiotherapy.