Deep learning-based prediction of Monte Carlo dose distribution for heavy ion therapy.

ABSTRACT:

Background and purpose

Current methods, like treatment planning system algorithms (TPSDose), lack accuracy, whereas Monte Carlo dose distribution (MCDose) is accurate but computationally intensive. We proposed a deep learning (DL) model for rapid prediction of Monte Carlo simulated dose distribution (MCDose) in heavy ion therapy (HIT).

Materials and methods

We developed a DL model - the Cascade Hierarchically Densely 3D U-Net (CHD U-Net) - to predict MCDose using computed tomography images and TPSDose of 67 head-and-neck patients and 30 thorax-and-abdomen patients. We also compared the results with other proton dose DL models and TPSDose.

Results

Compared to TPSDose, the gamma passing rate (GPR) improved by 16 % (1 %/1 mm). Notably, the model achieved 99 % and 97 % ac

SUBMITTER: He R 

PROVIDER: S-EPMC11929889 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature