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Magnetic resonance imaging-based proton dose calculation for pelvic tumors using deep learning.

Liheng Tian1, Laura Tsu1, Paulin Vehling1

  • 1Department of Physics, TU Dortmund University, Dortmund, Germany.

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|January 16, 2026
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Summary

Deep learning models enable MRI-only proton therapy dose calculations in the pelvis. The direct prediction pipeline shows robustness to MRI distortions, while the two-step pipeline offers lower dose prediction errors.

Keywords:
deep learningmagnetic resonance imagingproton dose

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence in Medicine

Background:

  • Magnetic resonance imaging (MRI)-only proton therapy offers superior soft tissue contrast and precise dose delivery.
  • Conventional dose calculation is hindered by the lack of electron density information in MRI scans.
  • Accurate dose calculation is crucial for effective proton therapy planning.

Purpose of the Study:

  • To investigate the feasibility of two deep learning (DL)-based MRI-only proton dose calculation pipelines for pelvic treatments.
  • To evaluate the robustness of these pipelines against MRI intensity distortions.
  • To compare the accuracy of DL-based dose predictions with Monte Carlo simulations.

Main Methods:

  • Two DL pipelines were developed: a two-step approach (MRI to synthetic CT, then dose prediction) and a direct approach (dose prediction directly on MRI).
  • Monte Carlo simulations were used to generate ground truth dose distributions for training and validation using MRI-CT data from 120 pelvic patients.
  • Pipeline performance was assessed using gamma pass rates and average relative error (ARE) for individual pencil beams and treatment plans, with introduced MRI intensity distortions.

Main Results:

  • Both pipelines achieved high gamma pass rates (>99.2%).
  • The two-step pipeline demonstrated low ARE (0.11% for pencil beams, 2.63% for treatment plans).
  • The direct pipeline showed higher ARE (0.16% for pencil beams, up to 6.11% for treatment plans) but exhibited robustness to MRI intensity distortions.

Conclusions:

  • Deep learning-based MRI-only proton dose calculation is feasible for pelvic region treatments.
  • The direct DL pipeline shows promise for learning MRI-to-dose mappings, though further optimization is needed.
  • The two-step DL pipeline provides accurate proton dose predictions with minimal errors, suitable for clinical implementation.