Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction

09/25/2019
by   Jo Schlemper, et al.
15

We present simple reconstruction networks for multi-coil data by extending deep cascade of CNN's and exploiting the data consistency layer. In particular, we propose two variants, where one is inspired by POCSENSE and the other is calibration-less. We show that the proposed approaches are competitive relative to the state of the art both quantitatively and qualitatively.

READ FULL TEXT
research
12/18/2019

Σ-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction

Purpose: To systematically investigate the influence of various data con...
research
07/12/2020

Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction

We present a deep network interpolation strategy for accelerated paralle...
research
04/08/2017

A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

Inspired by recent advances in deep learning, we propose a framework for...
research
05/26/2021

How to Calibrate Your Event Camera

We propose a generic event camera calibration framework using image reco...
research
12/11/2019

Σ-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction

We explore an ensembled Σ-net for fast parallel MR imaging, including pa...
research
06/11/2019

DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

This paper proposes a multi-channel image reconstruction method, named D...
research
04/01/2020

Robust Image Reconstruction with Misaligned Structural Information

Multi-modality (or multi-channel) imaging is becoming increasingly impor...

Please sign up or login with your details

Forgot password? Click here to reset