Results 241 to 250 of about 6,437,662 (327)
Natural Gas Purification Plants Based on Interpretive Structural Models and Bayesian Networks. [PDF]
Gong J+5 more
europepmc +1 more source
Abstract Purpose This study aims to develop a CycleGAN based denoising model to enhance the quality of low‐dose PET (LDPET) images, making them as close as possible to standard‐dose PET (SDPET) images. Methods Using a Philips Vereos PET/CT system, whole‐body PET images of fluorine‐18 fluorodeoxyglucose (18F‐FDG) were acquired from 37 patients to ...
Yang Liu, ZhiWu Sun, HaoJia Liu
wiley +1 more source
Abstract Purpose The aim of this work was to report on the optimization, commissioning, and validation of a beam model using a commercial independent dose verification software RadCalc version 7.2 (Lifeline Software Inc, Tyler, TX, USA), along with 4 years of experience employing RadCalc for offline and online monitor unit (MU) verification on the ...
Urszula Jelen+3 more
wiley +1 more source
Abstract Purpose/objective We propose a novel lattice deployment for spatially fractionated radiotherapy (SFRT) treatments. In this approach, a larger diameter high‐dose sphere is centrally placed in the bulky tumor mass and surrounded by smaller diameter high‐dose spheres.
Joshua Misa+2 more
wiley +1 more source
Risk Factor Analysis for Proximal Junctional Kyphosis in Neuromuscular Scoliosis: A Single-Center Study. [PDF]
Lange T+4 more
europepmc +1 more source
Processed meat and the risk of selected digestive tract and laryngeal neoplasms in Switzerland
Fabio Levi+4 more
openalex +1 more source
Abstract Purpose Stereotactic radiotherapy (SRT) is a highly effective treatment with precision for small, localized lesions. Proton therapy, characterized by the Bragg peak, offers superior dose conformity compared to photon‐based approaches. However, challenges remain in minimizing lateral penumbra and optimizing dose delivery, particularly for small
Chen‐Yu Chou+3 more
wiley +1 more source
Analysis of collapse risks under cut and cover method based on multi-state fuzzy Bayesian network. [PDF]
Liu P, Jin X, Shang Y, Zhu J.
europepmc +1 more source