Results 131 to 140 of about 393,742 (342)
IROC phantoms accurately detect MLC delivery errors
Abstract Purpose We evaluated the impact of random and whole‐bank multileaf collimator (MLC) delivery errors on dosimetric delivery accuracy in the Imaging and Radiation Oncology Core (IROC) phantom audits, as well as differences in delivery accuracy between the IROC phantom prescription and typical clinical fraction sizes.
Sharbacha S. Edward+5 more
wiley +1 more source
ABSORPTION OF SUBCUTANEOUSLY IMPLANTED HORMONE PELLETS [PDF]
P. M. F. Bishop, S. J. Folley
openalex +1 more source
A review of artificial intelligence in brachytherapy
Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen+4 more
wiley +1 more source
Evaluation of some Laboratory Results on the Formation and Combustion Biopellets [PDF]
The contribution comes out of the data obtained from graphical outputs analyses of the evaluation combustion process and from the experimental outcomes of compression of materials.
Viera Miklúšová
doaj
Abstract This study evaluates various radiotherapy techniques for treating metastatic brain tumor (BT), focusing on non‐coplanar volumetric modulated arc radiotherapy (NC‐VMAT), coplanar VMAT (C‐VMAT), Helical TomoTherapy (HT), CyberKnife (CK), Gamma Knife (GK), and ZAP‐X.
Toshihiro Suzuki+9 more
wiley +1 more source
Abstract Purpose The purpose of the present study was to evaluate the impact of bone relative electron density (rED) assignment on radiotherapy planning for the abdominal region. Methods Twenty patients who received abdominal radiotherapy using MR‐Linac and underwent magnetic resonance imaging (MRI) and computed tomography (CT) simulation were analyzed.
Kota Abe+4 more
wiley +1 more source
•Easy to weigh •easy to pour •free from dust •extremely pure PELLETS C.P. and U.S.P. [PDF]
openalex +1 more source
Abstract Background This study aims to develop a novel predictive model for determining human papillomavirus (HPV) presence in oropharyngeal cancer using computed tomography (CT). Current image‐based HPV prediction methods are hindered by high computational demands or suboptimal performance.
Junhua Chen+3 more
wiley +1 more source