MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach

by   Kamlesh Pawar, et al.

Purpose: The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper is to develop a standalone novel technique to suppress motion artefacts from MR images using a data-driven deep learning approach. Methods: A deep learning convolutional neural network (CNN) was developed to remove motion artefacts in brain MR images. A CNN was trained on simulated motion corrupted images to identify and suppress artefacts due to the motion. The network was an encoder-decoder CNN architecture where the encoder decomposed the motion corrupted images into a set of feature maps. The feature maps were then combined by the decoder network to generate a motion-corrected image. The network was tested on an unseen simulated dataset and an experimental, motion corrupted in vivo brain dataset. Results: The trained network was able to suppress the motion artefacts in the simulated motion corrupted images, and the mean percentage error in the motion corrected images was 2.69 effectively suppress the motion artefacts from the experimental dataset, demonstrating the generalisation capability of the trained network. Conclusion: A novel and generic motion correction technique has been developed that can suppress motion artefacts from motion corrupted MR images. The proposed technique is a standalone post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for a multitude of MR sequences.



page 15

page 16

page 17

page 19

page 20


Motion Artifact Reduction in Quantitative Susceptibility Mapping using Deep Neural Network

An approach to reduce motion artifacts in Quantitative Susceptibility Ma...

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

In MRI, motion artefacts are among the most common types of artefacts. T...

Unsupervised MR Motion Artifact Deep Learning using Outlier-Rejecting Bootstrap Aggregation

Recently, deep learning approaches for MR motion artifact correction hav...

Correcting inter-scan motion artefacts in quantitative R1 mapping at 7T

Purpose: Inter-scan motion is a substantial source of error in R_1 estim...

Prediction of progressive lens performance from neural network simulations

Purpose: The purpose of this study is to present a framework to predict ...

Deep correction of breathing-related artifacts in MR-thermometry

Real-time MR-imaging has been clinically adapted for monitoring thermal ...

Clinically Translatable Direct Patlak Reconstruction from Dynamic PET with Motion Correction Using Convolutional Neural Network

Patlak model is widely used in 18F-FDG dynamic positron emission tomogra...
This week in AI

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