Zobrazeno 1 - 10
of 559
pro vyhledávání: '"Nagpal, Prashant"'
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
Publikováno v:
In Radiologic Clinics of North America May 2024 62(3):509-525
Autor:
Randhawa, Mangun K., Sultana, Sadia, Stib, Matthew T., Nagpal, Prashant, Michel, Eriberto, Hedgire, Sandeep
Publikováno v:
In Radiologic Clinics of North America May 2024 62(3):453-471
Bilinear models that decompose dynamic data to spatial and temporal factors are powerful and memory-efficient tools for the recovery of dynamic MRI data. These methods rely on sparsity and energy compaction priors on the factors to regularize the rec
Externí odkaz:
http://arxiv.org/abs/2106.15785
Autor:
Priya, Sarv, Hartigan, Tyler, Perry, Sarah S., Goetz, Sawyer, Dalla Pria, Otavio Augusto Ferreira, Walling, Abigail, Nagpal, Prashant, Ashwath, Ravi, Bi, Xiaoming, Chitiboi, Teodora
Publikováno v:
In Academic Radiology April 2024 31(4):1643-1654
Autor:
Priya, Sarv, Dhruba, Durjoy D., Perry, Sarah S., Aher, Pritish Y., Gupta, Amit, Nagpal, Prashant, Jacob, Mathews
Publikováno v:
In Academic Radiology February 2024 31(2):503-513
We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that the images in the dataset are non-linear mappings of low-dimensional l
Externí odkaz:
http://arxiv.org/abs/2102.00034
We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-line
Externí odkaz:
http://arxiv.org/abs/2101.12366
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
We propose a deep self-learning algorithm to learn the manifold structure of free-breathing and ungated cardiac data and to recover the cardiac CINE MRI from highly undersampled measurements. Our method learns the manifold structure in the dynamic da
Externí odkaz:
http://arxiv.org/abs/1911.02492