Search Results for author: Prashant Nagpal

Found 6 papers, 1 papers with code

Joint alignment and reconstruction of multislice dynamic MRI using variational manifold learning

no code implementations21 Nov 2021 Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal, Sarv Priya, Rolf F Schulte, Mathews Jacob

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 Deep Bi-linear Unsupervised Regularization (DEBLUR)

no code implementations30 Jun 2021 Abdul Haseeb Ahmed, Prashant Nagpal, Mathews Jacob

Bilinear models that decompose dynamic data to spatial and temporal factors are powerful and memory-efficient tools for the recovery of dynamic MRI data.

Deep Generative SToRM model for dynamic imaging

no code implementations29 Jan 2021 Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal, Stanley Kruger, Mathews Jacob

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.

Time Series Time Series Analysis

Variational manifold learning from incomplete data: application to multislice dynamic MRI

no code implementations20 Jan 2021 Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal, Sarv Priya, Rolf Schulte, Mathews Jacob

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.

Imputation

Free-breathing and ungated cardiac cine using navigator-less spiral SToRM

2 code implementations16 Jan 2019 Abdul Haseeb Ahmed, Yasir Mohsin, Ruixi Zhou, Yang Yang, Michael Salerno, Prashant Nagpal, Mathews Jacob

An iterative kernel low-rank algorithm is introduced to estimate the manifold structure of the images, or equivalently the manifold Laplacian matrix, from the central k-space regions.

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