1.1k citations · 1.4k across the 51 of their papers we have counts for
60 papers
Multi-Scale Energy (MuSE) plug and play framework for inverse problems
Jyothi Rikhab Chand, Mathews Jacob
We introduce multi-scale energy models to learn the prior distribution of images, which can be used in inverse problems to derive the Maximum A Posteriori (MAP) estimate and to sam…
Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDL
Aniket Pramanik, Sampada Bhave, Saurav Sajib +2
Purpose: The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with mult…
Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)
Aniket Pramanik, Mathews Jacob
Model-based deep learning methods that combine imaging physics with learned regularization priors have been emerging as powerful tools for parallel MRI acceleration. The main focus…
Deep Factor Model: A Novel Approach for Motion Compensated Multi-Dimensional MRI
Yan Chen, James H. Holmes, Curtis Corum +2
Recent quantitative parameter mapping methods including MR fingerprinting (MRF) collect a time series of images that capture the evolution of magnetization. The focus of this work…
Plug-and-Play Deep Energy Model for Inverse problems
Jyothi Rikabh Chand, Mathews Jacob
We introduce a novel energy formulation for Plug- and-Play (PnP) image recovery. Traditional PnP methods that use a convolutional neural network (CNN) do not have an energy based f…
Prospectively accelerated dynamic speech MRI at 3 Tesla using a self-navigated spiral based manifold regularized scheme
Rushdi Zahid Rusho, Abdul Haseeb Ahmed, Stanley Kruger +6
This work proposes a self-navigated variable density spiral(VDS) based manifold regularization scheme to prospectively improve dynamic speech MRI at 3T. Short readout 1.3ms spirals…