A proof-of-concept study of direct magnetic resonance imaging-based proton dose calculation for brain tumors via neural networks with Monte Carlo-comparable accuracy.

ABSTRACT:

Background and purpose

Proton therapy currently relies on computed tomography (CT) imaging despite magnetic resonance imaging's (MRI) superior soft-tissue contrast. While synthetic CTs can be generated from magnetic resonance (MR) images, this introduces additional complexity. We present a deep learning-based dose engine enabling direct proton dose calculation from MR images to streamline workflows while maintaining Monte Carlo (MC)-level accuracy.

Materials and methods

Using paired MR-CT scans from 39 brain tumor patients (29/3/7 for training/validation/testing), we developed a deep learning framework using various sequence models for individual proton pencil beam dose prediction. The framework processes beam-eye-view patches from 2000 random beam configurations per patient

SUBMITTER: Li M 

PROVIDER: S-EPMC12275951 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature