Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging

04/05/2022
by   Hui Tao, et al.
1

Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology. Traditional linear reconstruction methods in parallel imaging often suffer from noise amplification. Recently, a non-linear robust artificial-neural-network for k-space interpolation (RAKI) exhibits superior noise resilience over other linear methods. However, RAKI performs poorly at high acceleration rates, and needs a large amount of autocalibration signals as the training samples. In order to tackle these issues, we propose a multi-weight method that implements multiple weighting matrices on the undersampled data, named as MW-RAKI. Enforcing multiple weighted matrices on the measurements can effectively reduce the influence of noise and increase the data constraints. Furthermore, we incorporate the strategy of multiple weighting matrixes into a residual version of RAKI, and form MW-rRAKI.Experimental compari-sons with the alternative methods demonstrated noticeably better reconstruction performances, particularly at high acceleration rates.

READ FULL TEXT

page 1

page 3

page 4

page 6

page 7

page 8

research
10/19/2022

A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation

Parallel imaging is a widely-used technique to accelerate magnetic reson...
research
05/08/2022

WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction

Parallel Imaging (PI) is one of the most im-portant and successful devel...
research
10/27/2019

GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction

Magnetic Resonance Image (MRI) acquisition is an inherently slow process...
research
01/10/2022

Iterative RAKI with Complex-Valued Convolution for Improved Image Reconstruction with Limited Scan-Specific Training Samples

MRI scan time reduction is commonly achieved by Parallel Imaging methods...
research
08/06/2019

Model-based Convolutional De-Aliasing Network Learning for Parallel MR Imaging

Parallel imaging has been an essential technique to accelerate MR imagin...
research
04/14/2020

End-to-End Variational Networks for Accelerated MRI Reconstruction

The slow acquisition speed of magnetic resonance imaging (MRI) has led t...
research
08/30/2023

Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation

In the field of parallel imaging (PI), alongside image-domain regulariza...

Please sign up or login with your details

Forgot password? Click here to reset