Deep learning-based synthetic-CT-free photon dose calculation in MR-guided radiotherapy: A proof-of-concept study.

ABSTRACT:

Background

In magnetic resonance imaging (MRI)-guided online adaptive radiotherapy, MRI lacks tissue attenuation information necessary for accurate dose calculations. Although deep learning (DL)-based synthetic computed tomography (CT) generation models have been developed to obtain CT density information from MRI, they usually do not meet the requirement of real-time plan adaptation.

Purpose

We propose a DL-based photon dose calculation method directly on 0.35 T MRI to skip synthetic CT generation and show its feasibility for prostate patient cases.

Methods

The 0.35 T planning MRI and deformed planning CT (registered to the planning MRI) of 34 prostate cancer patients treated with a 0.35 T magnetic resonance-linear accelerator (MR-Linac) were collected. The air cavit

SUBMITTER: Xiao F 

PROVIDER: S-EPMC12584934 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature