Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Mark Ibrahim"'
Patient-Specific Radiation Dose and Cancer Risk in Computed Tomography Examinations in Ondo, Nigeria
Autor:
Tajudeen Olaniyan, Caleb Aborisade, Fatai Balogun, Sulaiman Ogunsina, Aminu Saidu, Mark Ibrahim
Publikováno v:
Iranian Journal of Medical Physics, Vol 16, Iss 1, Pp 85-90 (2019)
Introduction: The dose in computed tomography (CT) often approach or exceed the optimum levels, thereby increasing the probability of cancer induction. With wide application of this diagnostic test, it is expedient to determine the effective dose (ED
Externí odkaz:
https://doaj.org/article/250b0b63db5543698d2b7da68c0f2694
Autor:
Qussay Marashly, Chaitra Gopinath, Alex Baher, Madan Acharya, Mobin Kheirkhahan, Benjamin Hardisty, Mossab Aljuaid, Ibrahim Tawhari, Mark Ibrahim, Alan K. Morris, Eugene G. Kholmovski, Brent D. Wilson, Nassir F. Marrouche, Mihail G. Chelu
Publikováno v:
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 10, Iss 7 (2021)
Background Esophageal thermal injury (ETI) is a byproduct of atrial fibrillation (AF) ablation using thermal sources. The most severe form of ETI is represented by atrioesophageal fistula, which has a high mortality rate. Late gadolinium enhancement
Externí odkaz:
https://doaj.org/article/e02056b4132c48c8b2a3cd9fc4ea72cb
Autor:
Ye Tian, Jason Mendes, Apoorva Pedgaonkar, Mark Ibrahim, Leif Jensen, Joyce D Schroeder, Brent Wilson, Edward V R DiBella, Ganesh Adluru
Publikováno v:
PLoS ONE, Vol 14, Iss 2, p e0211738 (2019)
PURPOSE:Dynamic contrast enhanced MRI of the heart typically acquires 2-4 short-axis (SA) slices to detect and characterize coronary artery disease. This acquisition scheme is limited by incomplete coverage of the left ventricle. We studied the feasi
Externí odkaz:
https://doaj.org/article/edf3429cddc249ad902b46bbdeb20885
Publikováno v:
SIAM Journal on Matrix Analysis and Applications. 43:1440-1468
Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net
Autor:
Johnathan Le, Ganesh Adluru, Ye Tian, Brent D. Wilson, Edward V. R. DiBella, Mark Ibrahim, Jason Mendes
Publikováno v:
Magn Reson Imaging
Purpose To develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) m
Autor:
Alex Baher, Ibrahim Tawhari, Mihail G. Chelu, Mobin Kheirkhahan, Alan K. Morris, Mossab Aljuaid, Brent D. Wilson, Madan Acharya, Nassir F. Marrouche, Qussay Marashly, Benjamin Hardisty, Chaitra Gopinath, Eugene G. Kholmovski, Mark Ibrahim
Publikováno v:
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
Background Esophageal thermal injury (ETI) is a byproduct of atrial fibrillation (AF) ablation using thermal sources. The most severe form of ETI is represented by atrioesophageal fistula, which has a high mortality rate. Late gadolinium enhancement
Autor:
Anwar, Tandar, Vikas, Sharma, Mark, Ibrahim, Tara, Jones, David, Morgan, Candice, Montzingo, James, Lee, Nathaniel, Birgenheier, Natalie, Silverton, Anu, Abraham, Frederick G P, Welt, Jason P, Glotzbach
Publikováno v:
The Journal of invasive cardiology. 33(1)
Transcatheter aortic valve implantation (TAVI) is now routinely performed in patients with aortic stenosis with low mortality and complication rates. Although periprocedural risks have been substantially minimized, procedure- and contrast-induced acu
Autor:
Brent D. Wilson, Ryan Avery, Suvai Gunasekaran, Daniel C. Lee, Daniel Kim, Rod S. Passman, Michael Markl, Eugene G. Kholmovski, Mark Ibrahim, Hassan Haji-Valizadeh
Publikováno v:
Radiol Cardiothorac Imaging
PURPOSE: To develop an accelerated three-dimensional (3D) late gadolinium enhancement (LGE) pulse sequence using balanced steady-state free precession readout with stack-of-stars k-space sampling and extra motion-state golden-angle radial sparse para
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1801a77df0b8d0a4bc34c1dcc0480741
https://europepmc.org/articles/PMC7605361/
https://europepmc.org/articles/PMC7605361/
Publikováno v:
ICAIF
Models of sequential data such as the recurrent neural network (RNN) often implicitly treat a sequence of data as having a fixed time interval between observations. They also do not account for group-level effects when multiple sequences are observed