17 citations · 30 across the 11 of their papers we have counts for
11 papers
Joint cardiac mapping and cardiac function estimation using a deep manifold framework
Qing Zou, Mathews Jacob
In this work, we proposed a continuous-acquisition strategy using a gradient echo (GRE) inversion recovery sequence based on spiral trajectories to simultaneously obtain the …
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…
Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks
Qing Zou, Mathews Jacob
Several imaging algorithms including patch-based image denoising, image time series recovery, and convolutional neural networks can be thought of as methods that exploit the manifo…
Sampling of surfaces and functions in high dimensional spaces
Qing Zou, Mathews Jacob
We introduce a sampling theoretic framework for the recovery of smooth surfaces and functions living on smooth surfaces from few samples. The proposed approach can be thought of as…
Sampling of Planar Curves: Theory and Fast Algorithms
Qing Zou, Sunrita Poddar, Mathews Jacob
We introduce a continuous domain framework for the recovery of a planar curve from a few samples. We model the curve as the zero level set of a trigonometric polynomial. We show th…