Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Ahmed, Abdul Haseeb"'
Autor:
Rusho, Rushdi Zahid, Ahmed, Abdul Haseeb, Kruger, Stanley, Alam, Wahidul, Meyer, David, Howard, David, Story, Brad, Jacob, Mathews, Lingala, Sajan Goud
This work proposes a self-navigated variable density spiral(VDS) based manifold regularization scheme to prospectively improve dynamic speech MRI at 3T. Short readout 1.3ms spirals were used to minimize off-resonance. A custom 16-channel speech coil
Externí odkaz:
http://arxiv.org/abs/2209.02768
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
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
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
Autor:
Ahmed, Abdul Haseeb, Qureshi, Ijaz M.
Motion free reconstruction of compressively sampled cardiac perfusion MR images is a challenging problem. It is due to the aliasing artifacts and the rapid contrast changes in the reconstructed perfusion images. In addition to the reconstruction limi
Externí odkaz:
http://arxiv.org/abs/1904.04982
Respiratory motion can cause strong blurring artifacts in the reconstructed image during MR acquisition. These artifacts become more prominent when use in the presence of undersampled data. Recently, compressed sensing (CS) is developed as an MR reco
Externí odkaz:
http://arxiv.org/abs/1904.04615
Autor:
Ahmed, Abdul Haseeb, Zhou, Ruixi, Yang, Yang, Nagpal, Prashant, Salerno, Michael, Jacob, Mathews
We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low-rank methods to recover free-breathing and ungated images from un
Externí odkaz:
http://arxiv.org/abs/1901.05542