6 citations · 6 across the 10 of their papers we have counts for
4 papers · 1 filter
Memory-efficient deep end-to-end posterior network (DEEPEN) for inverse problems
Jyothi Rikhab Chand, Mathews Jacob
End-to-End (E2E) unrolled optimization frameworks show promise for Magnetic Resonance (MR) image recovery, but suffer from high memory usage during training. In addition, these det…
Motion Compensated Unsupervised Deep Learning for 5D MRI
Joseph Kettelkamp, Ludovica Romanin, Davide Piccini +2
We propose an unsupervised deep learning algorithm for the motion-compensated reconstruction of 5D cardiac MRI data from 3D radial acquisitions. Ungated free-breathing 5D MRI simpl…
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…
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…