64 citations · 89 across the 19 of their papers we have counts for
9 papers · 1 filter
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
Structured low-rank (SLR) algorithms, which exploit annihilation relations between the Fourier samples of a signal resulting from different properties, is a powerful image reconstr…
Dynamic MRI using deep manifold self-learning
Abdul Haseeb Ahmed, Hemant Aggarwal, Prashant Nagpal +1
We propose a deep self-learning algorithm to learn the manifold structure of free-breathing and ungated cardiac data and to recover the cardiac CINE MRI from highly undersampled me…
Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction. The proposed scheme is a non-linear generalization of structured low rank (S…
J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
Hemant Kumar Aggarwal, Mathews Jacob
Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to recover MRI data from undersampled multichannel Fourier measurements, are widely used to reduce…
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…
Improved Reconstruction for high-resolution Multi-shot Diffusion Weighted Imaging
Merry Mani, Hemant Kumar Aggarwal, Vincent Magnotta +1
Purpose: To introduce a fast and improved direct reconstruction method for multi-shot diffusion weighted (msDW) scans for high-resolution studies. Methods:Multi-shot EPI methods ca…