Results 251 to 260 of about 7,229,609 (313)
Abstract Introduction Many artificial intelligence (AI) solutions have been proposed to enhance the radiotherapy (RT) workflow, but limited applications have been implemented to date, suggesting an implementation gap. One contributing factor to this gap is a misalignment between AI systems and their users.
Luca M. Heising+11 more
wiley +1 more source
Antibacterial properties of silver and gold nanoparticles synthesized using Cannabis sativa waste extract against Pseudomonas aeruginosa. [PDF]
Michailidu J+4 more
europepmc +1 more source
Abstract Purpose This study quantitatively evaluates bladder changes and their dosimetric impact during the on‐couch adaptive process on a commercial CBCT‐based online adaptive radiotherapy (CT‐gART) platform. Methods Data from 183 fractions of ten patients receiving online ART for pelvic cancers were analyzed retrospectively.
Ingrid Valencia Lozano+7 more
wiley +1 more source
Highlights from the Special Issue Titled "Recent Advances in Organic Chemistry: Molecules Synthesis and Reactions". [PDF]
Chahboun R, Justicia J.
europepmc +1 more source
Semi‐automated hippocampal avoidance whole‐brain radiotherapy planning
Abstract Background Hippocampal avoidance whole‐brain radiotherapy (HA‐WBRT) is designed to spare cognitive function by reducing radiation dose to the hippocampus during the treatment of brain metastases. Current manual planning methods can be time‐consuming and may vary in quality, necessitating the development of automated approaches to streamline ...
Dong Joo Rhee+9 more
wiley +1 more source
THE ENZYMATIC SYNTHESIS IN VITRO OF HYALURONIC ACID CHAINS
Luis Glaser, David Henry Brown
openalex +1 more source
Abstract Background Dual‐energy cone‐beam CT (DE‐CBCT) has become subject of recent interest due to the ability to produce virtual monoenergetic images (VMIs) with improved soft‐tissue contrast and reduced nonuniformity artifacts. However, efficient production and optimization of VMIs remains an under‐explored part of DE‐CBCT's application.
Andrew Keeler+4 more
wiley +1 more source
Abstract Background Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing deep learning‐based automatic segmentation methods rely on manually annotated data for network training. Purpose This study aims to develop an unsupervised tumor segmentation network smic‐GAN by using a similarity‐driven generative adversarial ...
Chengyijue Fang+2 more
wiley +1 more source