Stop Moving: MR Motion Correction As an Opportunity for Artificial Intelligence

Overview

Journal MAGMA

Publisher Springer

Date 2024 Feb 22

PMID 38386151

Authors

Zijian Zhou

Peng Hu

Haikun Qi

Affiliations

Soon will be listed here.

Abstract

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can seriously deteriorate the image quality. Various prospective and retrospective methods have been proposed for MRI motion correction, among which deep learning approaches have achieved state-of-the-art motion correction performance. This survey paper aims to provide a comprehensive review of deep learning-based MRI motion correction methods. Neural networks used for motion artifacts reduction and motion estimation in the image domain or frequency domain are detailed. Furthermore, besides motion-corrected MRI reconstruction, how estimated motion is applied in other downstream tasks is briefly introduced, aiming to strengthen the interaction between different research areas. Finally, we identify current limitations and point out future directions of deep learning-based MRI motion correction.

Citing Articles

A Physics-Informed Deep Learning Model for MRI Brain Motion Correction.

Safari M, Wang S, Eidex Z, Qiu R, Chang C, Yu D ArXiv. 2025; .

PMID: 39990792 PMC: 11844622.

References

Zaitsev M, MacLaren J, Herbst M . Motion artifacts in MRI: A complex problem with many partial solutions. J Magn Reson Imaging. 2015; 42(4):887-901. PMC: 4517972. DOI: 10.1002/jmri.24850. View

Ehman R, McNamara M, PALLACK M, Hricak H, Higgins C . Magnetic resonance imaging with respiratory gating: techniques and advantages. AJR Am J Roentgenol. 1984; 143(6):1175-82. DOI: 10.2214/ajr.143.6.1175. View

Lanzer P, Barta C, Botvinick E, WIESENDANGER H, Modin G, Higgins C . ECG-synchronized cardiac MR imaging: method and evaluation. Radiology. 1985; 155(3):681-6. DOI: 10.1148/radiology.155.3.4001369. View

Glover G, Pauly J . Projection reconstruction techniques for reduction of motion effects in MRI. Magn Reson Med. 1992; 28(2):275-89. DOI: 10.1002/mrm.1910280209. View

Pipe J . Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging. Magn Reson Med. 1999; 42(5):963-9. DOI: 10.1002/(sici)1522-2594(199911)42:5<963::aid-mrm17>3.0.co;2-l. View

MacLaren J, Herbst M, Speck O, Zaitsev M . Prospective motion correction in brain imaging: a review. Magn Reson Med. 2012; 69(3):621-36. DOI: 10.1002/mrm.24314. View

Olesen O, Paulsen R, Hojgaard L, Roed B, Larsen R . Motion tracking for medical imaging: a nonvisible structured light tracking approach. IEEE Trans Med Imaging. 2011; 31(1):79-87. DOI: 10.1109/TMI.2011.2165157. View

Aranovitch A, Haeberlin M, Gross S, Dietrich B, Reber J, Schmid T . Motion detection with NMR markers using real-time field tracking in the laboratory frame. Magn Reson Med. 2019; 84(1):89-102. DOI: 10.1002/mrm.28094. View

Barnwell J, Smith J, Castillo M . Utility of navigator-prospective acquisition correction technique (PACE) for reducing motion in brain MR imaging studies. AJNR Am J Neuroradiol. 2007; 28(4):790-1. PMC: 7977331. View

10.

Moghari M, Hu P, Kissinger K, Goddu B, Goepfert L, Ngo L . Subject-specific estimation of respiratory navigator tracking factor for free-breathing cardiovascular MR. Magn Reson Med. 2011; 67(6):1665-72. PMC: 4218740. DOI: 10.1002/mrm.23158. View

11.

Skare S, Hartwig A, Martensson M, Avventi E, Engstrom M . Properties of a 2D fat navigator for prospective image domain correction of nodding motion in brain MRI. Magn Reson Med. 2014; 73(3):1110-9. DOI: 10.1002/mrm.25234. View

12.

Uribe S, Muthurangu V, Boubertakh R, Schaeffter T, Razavi R, Hill D . Whole-heart cine MRI using real-time respiratory self-gating. Magn Reson Med. 2007; 57(3):606-13. DOI: 10.1002/mrm.21156. View

13.

Larson A, White R, Laub G, McVeigh E, Li D, Simonetti O . Self-gated cardiac cine MRI. Magn Reson Med. 2004; 51(1):93-102. PMC: 2396326. DOI: 10.1002/mrm.10664. View

14.

Stehning C, Bornert P, Nehrke K, Eggers H, Stuber M . Free-breathing whole-heart coronary MRA with 3D radial SSFP and self-navigated image reconstruction. Magn Reson Med. 2005; 54(2):476-80. DOI: 10.1002/mrm.20557. View

15.

Piccini D, Littmann A, Nielles-Vallespin S, Zenge M . Respiratory self-navigation for whole-heart bright-blood coronary MRI: methods for robust isolation and automatic segmentation of the blood pool. Magn Reson Med. 2012; 68(2):571-9. DOI: 10.1002/mrm.23247. View

16.

Odille F, Vuissoz P, Marie P, Felblinger J . Generalized reconstruction by inversion of coupled systems (GRICS) applied to free-breathing MRI. Magn Reson Med. 2008; 60(1):146-57. DOI: 10.1002/mrm.21623. View

17.

Johnson P, Drangova M . Conditional generative adversarial network for 3D rigid-body motion correction in MRI. Magn Reson Med. 2019; 82(3):901-910. DOI: 10.1002/mrm.27772. View

18.

Kustner T, Armanious K, Yang J, Yang B, Schick F, Gatidis S . Retrospective correction of motion-affected MR images using deep learning frameworks. Magn Reson Med. 2019; 82(4):1527-1540. DOI: 10.1002/mrm.27783. View

19.

Pawar K, Chen Z, Jon Shah N, Egan G . Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation. NMR Biomed. 2019; 35(4):e4225. DOI: 10.1002/nbm.4225. View

20.

Kromrey M, Tamada D, Johno H, Funayama S, Nagata N, Ichikawa S . Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning-based filter using convolutional neural network. Eur Radiol. 2020; 30(11):5923-5932. PMC: 7651696. DOI: 10.1007/s00330-020-07006-1. View