Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Johnathan Le"'
Publikováno v:
Tomography, Vol 10, Iss 5, Pp 660-673 (2024)
Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time–concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Pu
Externí odkaz:
https://doaj.org/article/429b85042e6b4a4f8259226aeede253f
Autor:
Edward DiBella, PhD, Johnathan Le, MSc, Jason Mendes, PhD, Konstantinos Sideris, MD, Erik Bieging, MD, Spencer Carter, MD, Ganesh Adluru, PhD
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 26, Iss , Pp 100264- (2024)
Externí odkaz:
https://doaj.org/article/77fa72ad990b4f9d80af94786d4ed4dc
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 26, Iss , Pp 100953- (2024)
Externí odkaz:
https://doaj.org/article/cd3614cf726940e89d9f32f119adadf2
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8cb50231c9a5173c5668e920e037c2bc
https://doi.org/10.1016/j.mri.2021.08.007
https://doi.org/10.1016/j.mri.2021.08.007