64 citations · 89 across the 16 of their papers we have counts for
11 papers · 1 filter
Off-the-grid model based deep learning (O-MODL)
Aniket Pramanik, Hemant Kumar Aggarwal, Mathews Jacob
We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies i…
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…
A Generalized Structured Low-Rank Matrix Completion Algorithm for MR Image Recovery
Yue Hu, Xiaohan Liu, Mathews Jacob
Recent theory of mapping an image into a structured low-rank Toeplitz or Hankel matrix has become an effective method to restore images. In this paper, we introduce a generalized s…
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…
Model-based free-breathing cardiac MRI reconstruction using deep learned \& STORM priors: MoDL-STORM
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar +1
We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) car…
Adaptive structured low rank algorithm for MR image recovery
Yue Hu, Xiaohan Liu, Mathews Jacob
We introduce an adaptive structured low rank algorithm to recover MR images from their undersampled Fourier coefficients. The image is modeled as a combination of a piecewise const…