Zobrazeno 1 - 10
of 412
pro vyhledávání: '"Uǧurbil, Kâmil"'
Autor:
Demirel, Omer Burak, Yaman, Burhaneddin, Dowdle, Logan, Moeller, Steen, Vizioli, Luca, Yacoub, Essa, Strupp, John, Olman, Cheryl A., Uğurbil, Kâmil, Akçakaya, Mehmet
High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved coverage with high spatio-temporal resolution. Simultaneous multi-slic
Externí odkaz:
http://arxiv.org/abs/2105.05827
Autor:
Demirel, Omer Burak, Yaman, Burhaneddin, Dowdle, Logan, Moeller, Steen, Vizioli, Luca, Yacoub, Essa, Strupp, John, Olman, Cheryl A., Uğurbil, Kâmil, Akçakaya, Mehmet
Functional MRI (fMRI) is commonly used for interpreting neural activities across the brain. Numerous accelerated fMRI techniques aim to provide improved spatiotemporal resolutions. Among these, simultaneous multi-slice (SMS) imaging has emerged as a
Externí odkaz:
http://arxiv.org/abs/2105.04532
Autor:
Tavaf, Nader, Radder, Jerahmie, Lagore, Russell L., Jungst, Steve, Grant, Andrea, Ugurbil, Kamil, Adriany, Gregor, Van de Moortele, Pierre Francois
Transmitter arrays play a critical role in ultra high field Magnetic Resonance Imaging (MRI), especially given the advantages made possible via parallel transmission (pTx) techniques. One of the challenges in design and construction of transmit array
Externí odkaz:
http://arxiv.org/abs/2103.07516
k-space undersampling is a standard technique to accelerate MR image acquisitions. Reconstruction techniques including GeneRalized Autocalibrating Partial Parallel Acquisition(GRAPPA) and its variants are utilized extensively in clinical and research
Externí odkaz:
http://arxiv.org/abs/2101.03135
Autor:
Tavaf, Nader, Lagore, Russell L., Jungst, Steve, Gunamony, Shajan, Radder, Jerahmie, Grant, Andrea, Moeller, Steen, Auerbach, Edward, Ugurbil, Kamil, Adriany, Gregor, Van de Moortele, Pierre-Francois
Publikováno v:
Magn Reson Med. 2021 pp 1-14
Purpose: Receive array layout, noise mitigation and B0 field strength are crucial contributors to signal-to-noise ratio (SNR) and parallel imaging performance. Here, we investigate SNR and parallel imaging gains at 10.5 Tesla (T) compared to 7T using
Externí odkaz:
http://arxiv.org/abs/2009.07163
Autor:
Yaman, Burhaneddin, Gu, Hongyi, Hosseini, Seyed Amir Hossein, Demirel, Omer Burak, Moeller, Steen, Ellermann, Jutta, Uğurbil, Kâmil, Akçakaya, Mehmet
Publikováno v:
NMR in Biomedicine, 2022
Self-supervised learning has shown great promise due to its capability to train deep learning MRI reconstruction methods without fully-sampled data. Current self-supervised learning methods for physics-guided reconstruction networks split acquired un
Externí odkaz:
http://arxiv.org/abs/2008.06029
Autor:
Yaman, Burhaneddin, Hosseini, Seyed Amir Hossein, Moeller, Steen, Ellermann, Jutta, Uğurbil, Kâmil, Akçakaya, Mehmet
Publikováno v:
Magnetic Resonance in Medicine, 2020
Purpose: To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully-sampled datasets. Theory and Methods: Self-supervised learning via data under-sampling (SSDU) for physics-guided deep learning
Externí odkaz:
http://arxiv.org/abs/1912.07669
Autor:
Jiang, Weixiong, Zhou, Zhen, Li, Guoshi, Yin, Weiyan, Wu, Zhengwang, Wang, Li, Ghanbari, Maryam, Li, Gang, Yap, Pew-Thian, Howell, Brittany R., Styner, Martin A., Yacoub, Essa, Hazlett, Heather, Gilmore, John H., Keith Smith, J., Ugurbil, Kamil, Elison, Jed T., Zhang, Han, Shen, Dinggang, Lin, Weili
Publikováno v:
In Developmental Cognitive Neuroscience October 2023 63
Autor:
Yaman, Burhaneddin, Hosseini, Seyed Amir Hossein, Moeller, Steen, Ellermann, Jutta, Uǧurbil, Kâmil, Akçakaya, Mehmet
Publikováno v:
Proceedings of IEEE ISBI, 2020
Deep learning (DL) has emerged as a tool for improving accelerated MRI reconstruction. A common strategy among DL methods is the physics-based approach, where a regularized iterative algorithm alternating between data consistency and a regularizer is
Externí odkaz:
http://arxiv.org/abs/1910.09116
Autor:
Hosseini, Seyed Amir Hossein, Zhang, Chi, Weingärtner, Sebastian, Moeller, Steen, Stuber, Matthias, Uǧurbil, Kâmil, Akçakaya, Mehmet
This study aims to accelerate coronary MRI using a novel reconstruction algorithm, called self-consistent robust artificial-neural-networks for k-space interpolation (sRAKI). sRAKI performs iterative parallel imaging reconstruction by enforcing coil
Externí odkaz:
http://arxiv.org/abs/1907.08137