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20152022
most citedStructured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning

64 citations · 89 across the 16 of their papers we have counts for

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cs.CV201964 cited

Structured Low-Rank Algorithms: Theory, MR Applications, and Links to Machine Learning

Mathews Jacob, Merry P. Mani, Jong Chul Ye

In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements…

cs.CV2018

MoDL-MUSSELS: Model-Based Deep Learning for Multi-Shot Sensitivity Encoded Diffusion MRI

Hemant Kumar Aggarwal, Merry P. Mani, Mathews Jacob

We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MRI images. The proposed al…

cs.CV2018

Calibration-free B0 correction of EPI data using structured low rank matrix recovery

Arvind Balachandrasekaran, Merry Mani, Mathews Jacob

We introduce a structured low rank algorithm for the calibration-free compensation of field inhomogeneity artifacts in Echo Planar Imaging (EPI) MRI data. We acquire the data using…

cs.CV2018

Free-breathing cardiac MRI using bandlimited manifold modelling

Sunrita Poddar, Yasir Mohsin, Deidra Ansah +3

We introduce a novel bandlimited manifold framework and an algorithm to recover freebreathing and ungated cardiac MR images from highly undersampled measurements. The image frames…

cs.CV2018

Recovery of Point Clouds on Surfaces: Application to Image Reconstruction

Sunrita Poddar, Mathews Jacob

We introduce a framework for the recovery of points on a smooth surface in high-dimensional space, with application to dynamic imaging. We assume the surface to be the zero-level s…

cs.CV20173 cited

Clustering of Data with Missing Entries using Non-convex Fusion Penalties

Sunrita Poddar, Mathews Jacob

The presence of missing entries in data often creates challenges for pattern recognition algorithms. Traditional algorithms for clustering data assume that all the feature values a…