Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Rowe, Daniel B."'
Autor:
Sakitis, Chase J, Rowe, Daniel B
In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can ta
Externí odkaz:
http://arxiv.org/abs/2405.15003
Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for under
Externí odkaz:
http://arxiv.org/abs/2401.06348
Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast
Externí odkaz:
http://arxiv.org/abs/2310.18536
Autor:
Mathew, Sunil, Rowe, Daniel B.
Neural network pruning is a highly effective technique aimed at reducing the computational and memory demands of large neural networks. In this research paper, we present a novel approach to pruning neural networks utilizing Bayesian inference, which
Externí odkaz:
http://arxiv.org/abs/2308.02451
The majority of model-based learned image reconstruction methods in medical imaging have been limited to uniform domains, such as pixelated images. If the underlying model is solved on nonuniform meshes, arising from a finite element method typical f
Externí odkaz:
http://arxiv.org/abs/2103.15138
Akademický článek
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Publikováno v:
The Annals of Applied Statistics, 2018 Sep 01. 12(3), 1451-1478.
Externí odkaz:
https://www.jstor.org/stable/26542581
Publikováno v:
In NeuroImage 15 May 2018 172:538-553
Autor:
Kociuba, Mary C., Rowe, Daniel B.
Publikováno v:
In Magnetic Resonance Imaging July 2016 34(6):765-770