Results 191 to 200 of about 12,304,366 (360)

Time-scale and branching ratios in sequential multifragmentation

, 1994
L. G. Moretto, L. Phair, K. Tso, K. Jing, G. J. Wozniak   +4 more
openalex   +2 more sources

Effect of Velocity Ratio on the Control of Large-Scale Structures in Mixing Layers.

, 1996
Toshio Miyauchi, Mamoru Tanahashi, Kei Matsuoka   +2 more
openalex   +2 more sources

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

Journal 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, Sarah Palmquist, Ken‐Pin Hwang, Jingfei Ma, Usama Salem, Jia Sun, Xinzeng Wang, Jong Bum Son, Randy Ernst, Peng Wei, Harmeet Kaur, Nir Stanietzky   +11 more
wiley   +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

Journal 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, Lars Fredrik Fjæra, Camilla Hanquist Stokkevåg, Perry Johnson, Mark Artz, Nancy Price Mendenhall, Curtis Bryant, Ludvig Paul Muren   +7 more
wiley   +1 more source

A review of artificial intelligence in brachytherapy

Journal 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, Richard L. J. Qiu, Tonghe Wang, Shadab Momin, Xiaofeng Yang   +4 more
wiley   +1 more source

A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Studies on deep learning dose prediction increasingly focus on 3D models with multiple input channels and data augmentation, which increases the training time and thus also the environmental burden and hampers the ease of re‐training. Here we compare 2D and 3D U‐Net models with clinical accepted plans to evaluate the appropriateness of
Rosalie Klarenberg, Nienke L. M. Bakx, Coen W. Hurkmans   +2 more
wiley   +1 more source

Characterization of a prototype rapid kilovoltage x‐ray image guidance system designed for a linear accelerator radiation therapy unit

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose This study evaluates a novel cone‐beam computed tomography (CBCT) imaging solution integrated onto a conventional C‐arm linear accelerator (linac) with increased gantry speed. The purpose is to assess the impact of improved imaging hardware and reconstruction algorithms on image quality.
Theodore Arsenault, Jonathan Arrue, Kenneth Gregg, Atallah Baydoun, Atefeh Rezai, Christian Langmack, Lauren Henke, Daniel E. Spratt, Rojano Kashani, Alex Price   +9 more
wiley   +1 more source

STUDY ON THE ESTIMATED METHOD OF DETERIORATION RATIO OF AIR-CONDITIONING SYSTEMS INSTALLED IN MIDDLE-SCALE OFFICE BUILDINGS

, 2003
Eiji Hara, Toshio OJIMA
openalex   +2 more sources

Low-Q2 scaling behavior of the form-factor ratios for the NΔ(1232)-transition [PDF]

, 2010
G. M. Vereshkov, N. Volchanskiy
openalex   +1 more source

Genetic architecture of anthocyanin pigment traits and purple spot (Stemphylium vesicarium) resistance in an F₁ pseudo-testcross population of asparagus. [PDF]

Plant Genome
Parajuli S, McDonald MR, Adhikari L, Wolyn DJ.   +3 more
europepmc   +1 more source