Cancer imaging : the official publication of the International Cancer Imaging Society
30909974
BACKGROUND: The purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging (DTI), fibre bundle/tract-based spatial statistics (TBSS) and machine learning methods to study changes in the white matter (WM) structure and whole b...
PURPOSE: Real-time image-guided adaptive radiation therapy (IGART) requires accurate marker segmentation to resolve three-dimensional (3D) motion based on two-dimensional (2D) fluoroscopic images. Most common marker segmentation methods require prior...
We have previously developed a robotic ultrasound imaging system for motion monitoring in abdominal radiation therapy. Owing to the slow speed of ultrasound image processing, our previous system could only track abdominal motions under breath-hold. T...
PURPOSE: To quickly and automatically propagate organ contours from pretreatment to fraction images in magnetic resonance (MR)-guided prostate external-beam radiotherapy.
INTRODUCTION: To create and clinically validate knowledge-based planning (KBP) models for gynaecologic (GYN) and rectal cancer patients. Assessment of ecologic generalisability and predictive validity of conventional planning versus single calculatio...
International journal of radiation oncology, biology, physics
38432285
PURPOSE: The capacity for machine learning (ML) to facilitate radiation therapy (RT) planning for primary brain tumors has not been described. We evaluated ML-assisted RT planning with regard to clinical acceptability, dosimetric outcomes, and planni...
This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we empl...
BACKGROUND: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since the...
To assess the performance of a probabilistic deep learning based algorithm for predicting inter-fraction anatomical changes in head and neck patients.A probabilistic daily anatomy model (DAM) for head and neck patients DAM (DAM) is built on the varia...