Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Sydney N. Williams"'
Autor:
Sydney N. Williams, Sarah Allwood-Spiers, Paul McElhinney, Gavin Paterson, Jürgen Herrler, Patrick Liebig, Armin M. Nagel, John E. Foster, David A. Porter, Shajan Gunamony
Publikováno v:
Frontiers in Physics, Vol 9 (2021)
Purpose: Parallel transmit technology for MRI at 7 tesla will significantly benefit from high performance transmit arrays that offer high transmit efficiency and low mutual coupling between the individual array elements. A novel dual-mode transmit ar
Externí odkaz:
https://doaj.org/article/c9a14fe43c10468f8e7ad7cc2c03ba54
Autor:
Jürgen Herrler, Sydney N. Williams, Patrick Liebig, Belinda Ding, Paul McElhinney, Sarah Allwood‐Spiers, Christian R. Meixner, Shajan Gunamony, Andreas Maier, Arnd Dörfler, Rene Gumbrecht, David A. Porter, Armin M. Nagel
Publikováno v:
Magnetic Resonance in Medicine. 89:1888-1900
Publikováno v:
Physics in medicine and biology.
This paper reviews the field of multiple or parallel radiofrequency (RF) transmission for magnetic resonance imaging (MRI). Currently the use of ultra-high field (UHF) MRI at 7 tesla and above is gaining popularity, yet faces challenges with non-unif
Publikováno v:
Magnetic Resonance in Medicine. 79:1377-1386
PURPOSE Spectrally selective "prewinding" radiofrequency pulses can counteract B0 inhomogeneity in steady-state sequences, but can only prephase a limited range of off-resonance. We propose spectral-spatial small-tip angle prewinding pulses that incr
Publikováno v:
Magnetic resonance in medicine. 79(3)
Spectrally selective "prewinding" radiofrequency pulses can counteract B0 inhomogeneity in steady-state sequences, but can only prephase a limited range of off-resonance. We propose spectral-spatial small-tip angle prewinding pulses that increase the
Publikováno v:
Machine Learning and Data Mining in Pattern Recognition ISBN: 9783642397110
MLDM
MLDM
Evaluation for probabilistic multiclass systems has predominately been done by converting data into binary classes. While effective in quantifying the classifier performance, binary evaluation causes a loss in ability to distinguish between individua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::588449737a199c2e85a15d6173941cf2
https://doi.org/10.1007/978-3-642-39712-7_49
https://doi.org/10.1007/978-3-642-39712-7_49