Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI

11/11/2021
by   Mohammed A. Al-masni, et al.
21

In this paper, we develop an efficient retrospective deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving of the image spatial details and hence improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans.

READ FULL TEXT

Authors

page 6

page 7

page 11

page 14

page 17

page 18

page 19

page 20

11/28/2020

Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning

In MRI, motion artefacts are among the most common types of artefacts. T...
07/18/2018

Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver

Purpose: To improve the quality of images obtained via dynamic contrast-...
10/12/2020

Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI

Patient motion during the magnetic resonance imaging (MRI) acquisition p...
10/09/2021

Learning MRI Artifact Removal With Unpaired Data

Retrospective artifact correction (RAC) improves image quality post acqu...
07/16/2020

Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy

Fetal MRI is heavily constrained by unpredictable and substantial fetal ...
04/27/2018

Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation

Many imaging tasks require global information about all pixels in an ima...

Code Repositories

MRI_Motion_Artifact_Correction_Self-Assisted_Priors

None


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.