Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches. 

https://www.nature.com/articles/s41597-022-01694-8.pdf

Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans

Motion represents one of the major challenges in magnetic resonance imaging (MRI).Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts.Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process.The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely.To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials.This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies.We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields. 

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review

Physiological and physical motions of the subjects, e.g., patients, are the primary sources of image artifacts in magnetic resonance imaging (MRI), causing geometric distortion, blurring, low signal-to-noise ratio, or ghosting. To overcome motion artifacts, various deep learning strategies, and models have been investigated to enable retrospective and prospective motion correction for MRI. This review article provides a survey on current deep learning-based rigid motion correction methods that have been used for MRI. Also, deep learning motion correction methods are compared to conventional motion correction methods and hybrid methods. Furthermore, we discuss the advantages and limitations of the current deep learning motion correction methods, leading to some suggestions for the future development of deep learning motion correction methods and their potential applications in improving clinical MRI. 

Deep Learning-Based Rigid Motion Correction for Magnetic Resonance Imaging: A Survey

Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Autofocusing, a classic optimization-based method to remove rigid motion artifacts. The method takes the best of both worlds: the optimization-based routine iteratively executes the blind demotion and deep learning-based prior penalizes for unrealistic restorations and speeds up the convergence. We validate the method on three models of motion trajectories, using synthetic and real noisy data. The method proves resilient to noise and anatomic structure variation, outperforming the state-of-the-art motion correction methods. 

Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging

The role of AI in prostate MRI quality and interpretation: Opportunities and challenges.

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.

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

PURPOSE
To enable acceleration in 3D multi-echo gradient echo (mGRE) acquisition for myelin water imaging (MWI) by combining joint parallel imaging (JPI) and joint deep learning (JDL).


METHODS
We implemented a multi-step reconstruction process using both advanced parallel imaging and deep learning network which can utilize joint spatio-temporal components between the multi-echo images to further accelerate 3D mGRE acquisition for MWI. In the first step, JPI was performed to estimate missing k-space lines. Next, JDL was implemented to reduce residual artifacts and produce high-fidelity reconstruction by using variable splitting optimization consisting of spatio-temporal denoiser block, data consistency block, and weighted average block. The proposed method was evaluated for MWI with 2D Cartesian uniform under-sampling for each echo, enabling scan times of up to approximately 2 minutes for 2 mm × 2 mm × 2 mm $2{\rm{mm}} \times 2{\rm{mm}} \times 2{\rm{mm}}$ 3D coverage.


RESULTS
The proposed method showed acceptable MWI quality with improved quantitative values compared to both JPI methods and JDL method individually. The improved performance of the proposed method was demonstrated by the low normalized mean square error and high-frequency error norm values of the reconstruction with high similarity to the fully-sampled MWI.


CONCLUSION
Joint spatio-temporal reconstruction approach by combining JPI and JDL can achieve high acceleration factors for 3D mGRE based MWI. This article is protected by copyright. All rights reserved.

Accelerated 3D myelin water imaging using joint spatio-temporal reconstruction.

Phase-based EPT algorithm is extremely sensitive to noise. Although many studies have investigated such as linear(Gaussian filter) or non-linear filter(TV norm) to cope with amplification, textured noise and staircasing effect still remain in phase image, which lead to conductivity error such as broadening boundary artifact or high std value in reconstructed conductivity maps. In this study, we propose a deep prior based denoising method, which achieve to not only suppress instability brought by noise amplification but reduce boundary error. 

Deep prior for suppressing noise amplification and edge preservation in Phase-based EPT with Low-SNR image

A Deep learning informed Polynomial Fitting Approach for Electrical Properties Tomography

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

Accelerated 3D myelin water imaging using joint spatio-temporal reconstruction.

Deep prior for suppressing noise amplification and edge preservation in Phase-based EPT with Low-SNR image

A Deep learning informed Polynomial Fitting Approach for Electrical Properties Tomography