Deep Learning-Based Heterogeneity Correction of the Homogeneous Dose Distribution for Single Brain Tumors in Gamma Knife Radiosurgery.

ABSTRACT:

Purpose

Heterogeneity correction is vital in radiation therapy treatment planning to ensure accurate dose delivery. Brain cancer stereotactic treatments, like Gamma Knife radiosurgery (GKRS), often rely on homogeneous water-based calculations despite the potential heterogeneity impact near bony structures. This study aims to develop a method for generating synthetic dose plans incorporating heterogeneity effects without additional computed tomography (CT) scans.

Methods and materials

Magnetic resonance imaging and CT images, TMR10-based, and convolution-based dose distributions were used from 100 retrospectively collected and 22 prospectively collected GKRS patients. A conditional Generative Adversarial Network was trained to translate TMR10 into synthetic convolution (sConv

SUBMITTER: Lee S 

PROVIDER: S-EPMC11994306 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature