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Found 10 papers, 0 papers with code
Efficient online cross-covariance monitoring with incremental SVD: An approach for the detection of emerging dependency patterns in IoT systems
The development of the manufacturing systems has made it increasingly necessary to monitor the data generated by multiple interconnected subsystems with rapid incoming of samples.
Joint cardiac $T_1$ mapping and cardiac function estimation using a deep manifold framework
Once the network is trained, we can excite the latent vectors (the estimated motion signals and the contrast signal) in any way as we wanted to generate the image frames in the time series.
Dynamic imaging using Motion-Compensated SmooThness Regularization on Manifolds (MoCo-SToRM)
The template image volume, the parameters of the generator, and the latent vectors are learned directly from the k-t space data in an unsupervised fashion.
Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP
The deep image prior (DIP) algorithm was introduced for single-shot image recovery, completely eliminating the need for training data.
Joint alignment and reconstruction of multislice dynamic MRI using variational manifold learning
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.
Dynamic imaging using motion-compensated smoothness regularization on manifolds (MoCo-SToRM)
The images at each time instant are modeled as the deformed version of an image template using the above motion fields.
Deep Generative SToRM model for dynamic imaging
Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well as the latent vectors are jointly estimated from the undersampled measurements using stochastic gradient descent.
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model
The proposed scheme brings in the spatial regularization provided by the convolutional network.
Variational manifold learning from incomplete data: application to multislice dynamic MRI
Most of the current self-gating and manifold cardiac MRI approaches consider the independent recovery of images from each slice; these methods are not capable of exploiting the inter-slice redundancies in the datasets and require sophisticated post-processing or manual approaches to align the images from different slices.
Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks
The low-rank property of the features is used to determine the number of measurements needed to recover the surface.
