Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Wallace Loos"'
Autor:
Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos, Nikita Moriakov, Matthan Caan, George Yiasemis, Lívia Rodrigues, Alexandre Lopes, Helio Pedrini, Letícia Rittner, Maik Dannecker, Viktor Studenyak, Fabian Gröger, Devendra Vyas, Shahrooz Faghih-Roohi, Amrit Kumar Jethi, Jaya Chandra Raju, Mohanasankar Sivaprakasam, Mike Lasby, Nikita Nogovitsyn, Wallace Loos, Richard Frayne, Roberto Souza
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quali
Externí odkaz:
https://doaj.org/article/6261672881b846e09b8609e567b018fc
Publikováno v:
Magnetic Resonance in Medicine. 87:1561-1573
PURPOSE To develop a deep-learning model that leverages the spatial and temporal information from dynamic contrast-enhanced magnetic resonance (DCE MR) brain imaging in order to automatically estimate a vascular function (VF) for quantitative pharmac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e28577bc78293d79a8506db4d1576a52
https://doi.org/10.1002/mrm.29054
https://doi.org/10.1002/mrm.29054
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 14:1126-1136
Deep learning models have shown potential for reconstructing undersampled, multi-channel magnetic resonance (MR) image acquisitions. Recently proposed methods, however, have not leveraged information from prior subject-specific MR imaging sessions. S
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7da0393a235b6cea842c2e9dcc41a6ae
https://doi.org/10.1109/jstsp.2020.3001525
https://doi.org/10.1109/jstsp.2020.3001525
Autor:
Richard Frayne, Roberto Souza, Mariana P. Bento, Kevin J. Chung, R. Marc Lebel, Nikita Nogovitsyn, Wallace Loos
Publikováno v:
Magnetic Resonance Imaging. 71:140-153
The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were evaluated for multi-cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42bae4b2a80169edf32d96dbadf2e026
https://doi.org/10.1016/j.mri.2020.06.002
https://doi.org/10.1016/j.mri.2020.06.002