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...
Journal of radiological protection : official journal of the Society for Radiological Protection
Dec 12, 2025
Protecting medical personnel from the harmful effects of scattered ionising radiation during x-ray-guided procedures is a critical concern. Due to the complex and invisible nature of x-rays, monitoring radiation exposure has been challenging. Existin...
This study proposes Scout-Dose-TCM for direct, prospective estimation of organ-level and effective doses under tube current modulation (TCM) and compares its performance with two established methods.Contrast-enhanced chest-abdomen-pelvis CT exams fro...
. 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 27, 2025
Sparse-view low-dose computed tomography (LDCT) imaging poses difficulties in preserving image quality while reducing radiation exposure. Recent research has focused extensively on artificial intelligence (AI) to reduce artifacts in LDCT. This paper ...
This study aimed to determine the optimized scan time and injected activity regimen for clinical Ga DOTATATE PET/CT in neuroendocrine tumor imaging through an experimental approach without using machine learning techniques.A NEMA PET body phantom was...
OBJECTIVE: To evaluate the feasibility of using contrast enhancement boost (CE-Boost) combined with super-resolution deep learning reconstruction (SR-DLR) to reduce contrast agent dosage in pediatric patients with congenital heart disease (CHD).
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...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.