64 citations · 89 across the 19 of their papers we have counts for
4 papers · 1 filter
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…