Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Matthew J. Riblett"'
Publikováno v:
Medical Physics. 47:99-109
PURPOSE To develop and evaluate a method to automatically identify and quantify deformable image registration (DIR) errors between lung computed tomography (CT) scans for quality assurance (QA) purposes. METHODS We propose a deep learning method to f
Autor:
Y Wu, Matthew C. Schmidt, Nels C. Knutson, Rebecca Nichole Mahon, Francisco J. Reynoso, Erno Sajo, Matthew J. Riblett, Piotr Zygmanski, Mahmoud M.M. Yaqoub, Baozhou Sun, Marian Jandel, Caleb A. Raman, Yao Hao
Publikováno v:
Journal of Applied Clinical Medical Physics
Purpose Linear accelerator quality assurance (QA) in radiation therapy is a time consuming but fundamental part of ensuring the performance characteristics of radiation delivering machines. The goal of this work is to develop an automated and standar
Autor:
Paul J. Keall, Xun Jia, Zhuoran Jiang, Bin Li, Lei Ren, Matthew J. Riblett, Chun-Chien Shieh, Cyril Mory, Xiaoning Liu, Simon Rit, Yawei Zhang, Geoffrey D. Hugo, Yesenia Gonzalez
Publikováno v:
Medical Physics
Med Phys
Med Phys
Purpose Currently, four-dimensional (4D) cone-beam computed tomography (CBCT) requires a 3-4 min full-fan scan to ensure usable image quality. Recent advancements in sparse-view 4D-CBCT reconstruction have opened the possibility to reduce scan time a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8416735dc3a60b36bf2d3936e3a1572b
https://hdl.handle.net/2123/22845
https://hdl.handle.net/2123/22845
PURPOSE: To demonstrate the feasibility of using a purely data-driven, a posteriori respiratory motion modeling and reconstruction compensation method to improve 4D-CBCT image quality under clinically relevant image acquisition conditions. METHODS: E
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b298ff7814f902d198ec91b4b4e2b98
https://europepmc.org/articles/PMC6203328/
https://europepmc.org/articles/PMC6203328/
Autor:
Wei Ji, Lin Su, Tianyu Liu, Mark S. Shephard, Christopher D. Carothers, Deyang Gu, Mannudeep K. Kalra, Xining Du, Bob Liu, X. George Xu, Matthew J. Riblett, Forrest B. Brown
Publikováno v:
SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo.
The Monte Carlo radiation transport community faces a number of challenges associated with peta- and exa-scale computing systems that rely increasingly on heterogeneous architectures involving hardware accelerators such as GPUs and Xeon Phi coprocess
Publikováno v:
Medical Physics. 43:3355-3355
Purpose: To develop a hands-on learning experience that explores the radiological and structural properties of everyday items and applies this knowledge to design a simple phantom for radiotherapy exercises. Methods: Students were asked to compile a
Publikováno v:
Medical Physics. 43:3897-3897
Purpose: To evaluate the performance of a 4D-CBCT registration and reconstruction method that corrects for respiratory motion and enhances image quality under clinically relevant conditions. Methods: Building on previous work, which tested feasibilit
Publikováno v:
Medical Physics. 42:3730-3730
Purpose: To develop and compare four 4D-CBCT reconstruction methods that correct for respiratory motion and enhance image quality. Methods: Four motion-compensation workflows were developed employing a combination of deformable image registration (DI
Publikováno v:
Medical Physics. 40:475-475
Purpose: Monte Carlo (MC) simulation provides the most accurate results for CT dose calculations. Yet it is a time‐consuming task which often takes long time to run on CPUs. Using the recently developed hardware accelerators such as Graphics Proces