Results 221 to 230 of about 5,629,721 (306)

Does dose calculation algorithm affect the dosimetric accuracy of synthetic CT for MR‐only radiotherapy planning in brain tumors?

open access: yesJournal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose This study compares the dosimetric accuracy of deep‐learning‐based MR synthetic CT (sCT) in brain radiotherapy between the Analytical Anisotropic Algorithm (AAA) and AcurosXB (AXB). Additionally, it proposes a novel metric to predict the dosimetric accuracy of sCT for individual post‐surgical brain cases.
Jeffrey C. F. Lui   +3 more
wiley   +1 more source

T2‐weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study

open access: yesJournal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose To compare image quality and clinical utility of a T2‐weighted (T2W) 3‐dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).
Dan Nguyen   +11 more
wiley   +1 more source

Advances in cancer mechanobiology: Metastasis, mechanics, and materials. [PDF]

open access: yesAPL Bioeng
Clevenger AJ   +5 more
europepmc   +1 more source

Modelling of a double‐scattering proton therapy nozzle using the FLUKA Monte Carlo code and analysis of linear energy transfer in patients treated for prostate cancer

open access: yesJournal of Applied Clinical Medical Physics, EarlyView.
Abstract Background The dose‐averaged linear energy transfer (LETD) in proton therapy (PT) has in pre‐clinical studies been linked to the relative biological effectiveness (RBE) of protons. Until recently, the most common PT delivery method in prostate cancer has been double‐scattered PT, with LETD only available through dedicated Monte Carlo (MC ...
Rasmus Klitgaard   +7 more
wiley   +1 more source

A review of artificial intelligence in brachytherapy

open access: yesJournal of Applied Clinical Medical Physics, EarlyView.
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

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