17 citations · 30 across the 11 of their papers we have counts for
7 papers · 1 filter
Dynamic imaging using Motion-Compensated SmooThness Regularization on Manifolds (MoCo-SToRM)
Qing Zou, Luis A. Torres, Sean B. Fain +3
We introduce an unsupervised motion-compensated reconstruction scheme for high-resolution free-breathing pulmonary MRI. We model the image frames in the time series as the deformed…
Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP
Maneesh John, Hemant Kumar Aggarwal, Qing Zou +1
Deep learning algorithms that rely on extensive training data are revolutionizing image recovery from ill-posed measurements. Training data is scarce in many imaging applications,…
Joint alignment and reconstruction of multislice dynamic MRI using variational manifold learning
Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal +3
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…
Dynamic imaging using motion-compensated smoothness regularization on manifolds (MoCo-SToRM)
Qing Zou, Luis A. Torres, Sean B. Fain +1
We introduce an unsupervised deep manifold learning algorithm for motion-compensated dynamic MRI. We assume that the motion fields in a free-breathing lung MRI dataset live on a ma…
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model
Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal +2
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…
Deep Generative SToRM model for dynamic imaging
Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal +2
We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed gene…
Variational manifold learning from incomplete data: application to multislice dynamic MRI
Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal +3
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 dataset…