Deep inspiration breath hold (DIBH) with surface supervising is a common technique for cardiac dose reduction in left breast cancer radiotherapy. Surface supervision accuracy relies on the characteristics of surface region. In this study, a convoluti...
PURPOSE: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve ...
The goal of this study is to demonstrate the feasibility of a novel fully-convolutional volumetric dose prediction neural network (DoseNet) and test its performance on a cohort of prostate stereotactic body radiotherapy (SBRT) patients. DoseNet is su...
PURPOSE: To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorith...
PURPOSE: To develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity-modulated radiation therapy trea...
Journal of applied clinical medical physics
Nov 12, 2018
PURPOSE: Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning ...
Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult task for head-and-neck cancer patients where detailed knowledge of the pathways of microscopic tumor...
BACKGROUND: In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OARs) depicted in computed tomography (CT) images of lung cancer patients, and the results were compar...
Artificial intelligence (AI) is beginning to transform IMRT treatment planning for head and neck patients. However, the complexity and novelty of AI algorithms make them susceptible to misuse by researchers and clinicians. Understanding nuances of ne...
Journal of applied clinical medical physics
Oct 19, 2018
Knowledge-based planning (KBP) can be used to improve plan quality, planning speed, and reduce the inter-patient plan variability. KPB may also identify and reduce systematic variations in VMAT plans, something very important in multi-institutional c...
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