Zobrazeno 1 - 10 of 424 pro vyhledávání: '"Schulte Rolf"'
1
Report
Pinv-Recon: Generalized MR Image Reconstruction via Pseudoinversion of the Encoding Matrix
Autor: Yeung, Kylie, Gleeson, Fergus V, Schulte, Rolf F, McIntyre, Anthony, Serres, Sebastien, Morris, Peter, Auer, Dorothee, Tyler, Damian J, Grist, James T, Wiesinger, Florian
Purpose: To present a novel generalized MR image reconstruction based on pseudoinversion of the encoding matrix (Pinv-Recon) as a simple yet powerful method, and demonstrate its computational feasibility for diverse MR imaging applications. Methods:
Externí odkaz: http://arxiv.org/abs/2410.06129
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7
Report
Joint alignment and reconstruction of multislice dynamic MRI using variational manifold learning
Autor: Zou, Qing, Ahmed, Abdul Haseeb, Nagpal, Prashant, Priya, Sarv, Schulte, Rolf F, Jacob, Mathews
Free-breathing cardiac MRI schemes are emerging as competitive alternatives to breath-held cine MRI protocols, enabling applicability to pediatric and other population groups that cannot hold their breath. Because the data from the slices are acquire
Externí odkaz: http://arxiv.org/abs/2111.10889
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8
Report
Variational manifold learning from incomplete data: application to multislice dynamic MRI
Autor: Zou, Qing, Ahmed, Abdul Haseeb, Nagpal, Prashant, Priya, Sarv, Schulte, Rolf, Jacob, Mathews
Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. Once learned, the density can be used for a variety of tasks, inclu
Externí odkaz: http://arxiv.org/abs/2101.08196
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9
Report
Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging
Autor: Gómez, Pedro A., Cencini, Matteo, Golbabaee, Mohammad, Schulte, Rolf F., Pirkl, Carolin, Horvath, Izabela, Fallo, Giada, Peretti, Luca, Tosetti, Michela, Menze, Bjoern H., Buonincontri, Guido
Novel methods for quantitative, transient-state multiparametric imaging are increasingly being demonstrated for assessment of disease and treatment efficacy. Here, we build on these by assessing the most common Non-Cartesian readout trajectories (2D/
Externí odkaz: http://arxiv.org/abs/2001.07173
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10
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