This study aims to develop a multi-modality-guided dose prediction (MMDP)-based auto-planning algorithm for functional lung avoidance radiotherapy (FLART) guided by voxel-wise lung function images.The proposed auto-planning algorithm consists of a no...
During radiotherapy, the radiation dose delivered to circulating blood can result in radiation-induced lymphopenia, which is correlated with adverse clinical outcomes like lower survival. Increasingly complex models to simulate radiation dose deliver...
This study proposes a workflow integrating the aperture shape controller (ASC) in the Varian Eclipse system with a machine learning-based verification prediction model to improve gamma passing rates (GPRs) of LATTICE Radiotherapy (LRT) plans and redu...
This study aims to uncover the opaque decision-making process of an artificial intelligence (AI) agent for automatic treatment planning.We examined a previously developed AI agent based on the actor-critic with experience replay (ACER) network, which...
. Accurate dose accumulation relies on deformable image registration (DIR) to track dose across multiple images. However, DIR introduces uncertainties that can impact cumulative dose distributions. In this study, we present a probabilistic framework ...
Accurate dose calculation on cone beam computed tomography (CBCT) images is essential for modern proton treatment planning workflows, particularly when accounting for inter-fractional anatomical changes in adaptive treatment scenarios. Traditional CB...
Biomedical physics & engineering express
Nov 25, 2025
The fidelity of dose distribution prediction is paramount for radiotherapy planning. While existing deep learning-based methods have obtained noteworthy performance, most of them pursue the accurate prediction of global dose distribution but neglect ...
Online adaptation in magnetic resonance imaging-guided radiotherapy (MRgRT) for lung cancer is hindered by time-consuming organs-at-risk (OARs) recontouring on daily MR images (dMRIs) and inter-/intra-observer variability. Deep learning auto-segmenta...
To develop and evaluate a deep reinforcement learning (RL) framework for rapid and automatic machine parameter optimization of volumetric modulated arc therapy (VMAT) treatment plans for localized prostate cancer.A multi-task policy network combining...
Advances in radiotherapy have increased treatment plan complexity, making manual quality evaluation more subjective and variable. While deep learning approaches offer automation in planning, evaluation remains a manual bottleneck. Existing indices ev...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.