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A survey on Motion Artifact Correction in Magnetic Resonance Imaging for Improved Diagnostics

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Abstract

Motion artifacts occur in magnetic resonance imaging (MRI) due to the motion or movement of the object being scanned. Motion artifacts can have various origins such as voluntary or involuntary patient movement, faulty components, improper software configuration, etc. Blurry MRI scans are generated due to the presence of motion artifacts. In some cases motion artifact induced MRI scans can tamper with crucial information and consequently leads to a faulty diagnosis. We attempt to provide a review of current suggested technologies (such as, deep learning, and encoding) used to remove motion artifacts from the MRI scans. The different approaches are summarized in brief, with their advantages and disadvantages. We expect the readers to be better equipped with the knowledge and tools available for magnetic resonance image artifact removal. We have also proposed many simple cases and their corresponding solutions in the discussion section of this manuscript. It was found that deep learning with CNN can reach maximum accuracy reported in motion artifact correction is about 97%. With the advancements in the field of artificial intelligence, and more and more sample data available (due to non-linear expansion of big data and cloud storage capabilities), it is expected that motion artifacts could soon be eliminated quite accurately.

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References

  1. 1234.info: motion artifact correction with motionSCOUT (under development). 2019. https://www.robinmedical.com/motion_artifact.html.

  2. Al-Masni MA, Lee S, Al-Shamiri AK, Gho SM, Choi YH, Kim DH. A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI. Comput Biol Medicine. 2023;153: 106553.

    Article  Google Scholar 

  3. Al-Masni MA, Lee S, Yi J, Kim S, Gho SM, Choi YH, Kim DH. Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI. NeuroImage. 2022;259: 119411.

    Article  PubMed  Google Scholar 

  4. Atkinson D, Hill DL, Stoyle PN, Summers PE, Clare S, Bowtell R, Keevil SF. Automatic compensation of motion artifacts in MRI. Magn Reson Med. 1999;41(1):163–70.

    Article  CAS  PubMed  Google Scholar 

  5. Atkinson D, Hill DL, Stoyle PN, Summers PE, Keevil SF. Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. IEEE Trans Med Imaging. 1997;16(6):903–10.

    Article  CAS  PubMed  Google Scholar 

  6. Bailes D, Gilderdale D, Bydder G, Collins A, Firmin D. Respiratory ordered phase encoding (ROPE): a method for reducing respiratory motion artefacts in MR imaging. J Comput Assist Tomogr. 1985;9(4):835–8.

    Article  CAS  PubMed  Google Scholar 

  7. Bernstein MA, Huston J III, Ward HA. Imaging artifacts at 30 T. J Magn Reson Imaging. 2006;24(4):735–46.

    Article  PubMed  Google Scholar 

  8. Blumenthal JD, Zijdenbos A, Molloy E, Giedd JN. Motion artifact in magnetic resonance imaging: implications for automated analysis. Neuroimage. 2002;16(1):89–92.

    Article  PubMed  Google Scholar 

  9. Brau AC, Brittain JH. Generalized self-navigated motion detection technique: preliminary investigation in abdominal imaging. Magn Reson Med. 2006;55(2):263–70.

    Article  PubMed  Google Scholar 

  10. Castellanos NP, Makarov VA. Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods. 2006;158(2):300–12.

    Article  PubMed  Google Scholar 

  11. Chung H, Kim J, Yoon JH, Lee JM, Ye JC. Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning. 2021. arXiv preprint arXiv:2105.00240.

  12. Ciric R, Rosen AF, Erus G, Cieslak M, Adebimpe A, Cook PA, Bassett DS, Davatzikos C, Wolf DH, Satterthwaite TD. Mitigating head motion artifact in functional connectivity MRI. Nat Protoc. 2018;13(12):2801–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cui L, Song Y, Wang Y, Wang R, Wu D, Xie H, Li J, Yang G. Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis. PLoS One. 2023;18(1): e0278668.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dietrich TJ, Ulbrich EJ, Zanetti M, Fucentese SF, Pfirrmann CW. Propeller technique to improve image quality of MRI of the shoulder. Am J Roentgenol. 2011;197(6):W1093–100.

    Article  Google Scholar 

  15. Duffy BA, Zhang W, Tang H, Zhao L, Law M, Toga AW, Kim H. Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion. 2018.

  16. Duffy BA, Zhang W, Tang H, Zhao L, Law M, Toga AW, Kim H. Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion. In: Medical imaging with deep learning. 2022.

  17. Duffy BA, Zhao L, Sepehrband F, Min J, Wang DJ, Shi Y, Toga AW, Kim H, Initiative ADN, et al. Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions. NeuroImage. 2021;230: 117756.

    Article  PubMed  Google Scholar 

  18. Duong S, Phung SL, Bouzerdoum A, Taylor HB, Puckett A, Schira MM. Susceptibility artifact correction for sub-millimeter fMRI using inverse phase encoding registration and T1 weighted regularization. J Neurosci Methods. 2020;336: 108625.

    Article  CAS  PubMed  Google Scholar 

  19. Ehman RL, Felmlee JP. Adaptive technique for high-definition MR imaging of moving structures. Radiology. 1989;173(1):255–63.

    Article  CAS  PubMed  Google Scholar 

  20. Elster A. Phase encoding direction. 2021. http://mriquestions.com/choosing-pefe-direction.html.

  21. Fantini I, Yasuda C, Bento M, Rittner L, Cendes F, Lotufo R. Automatic MR image quality evaluation using a deep CNN: a reference-free method to rate motion artifacts in neuroimaging. Comput Med Imaging Graph. 2021;90: 101897.

    Article  PubMed  Google Scholar 

  22. Goto M, Abe O, Miyati T, Yamasue H, Gomi T, Takeda T. Head motion and correction methods in resting-state functional MRI. Magn Reson Med Sci. 2015;rev–2015.

  23. Graves MJ, Mitchell DG. Body MRI artifacts in clinical practice: a physicist’s and radiologist’s perspective. J Magn Reson Imaging. 2013;38(2):269–87.

    Article  PubMed  Google Scholar 

  24. Hedley M, Yan H, Rosenfeld D. Motion artifact correction in MRI using generalized projections. IEEE Trans Medical Imaging. 1991;10(1):40–6.

    Article  CAS  PubMed  Google Scholar 

  25. Herrick RC, Hayman LA, Taber KH, Diaz-Marchan PJ, Kuo MD. Artifacts and pitfalls in MR imaging of the orbit: a clinical review. Radiographics. 1997;17(3):707–24.

    Article  CAS  PubMed  Google Scholar 

  26. de Hollander G, Keuken MC, van der Zwaag W, Forstmann BU, Trampel R. Comparing functional MRI protocols for small, iron-rich basal ganglia nuclei such as the subthalamic nucleus at 7 T and 3 T. Hum Brain Map. 2017;38(6):3226–48.

    Article  Google Scholar 

  27. Kaur P, Kumaran SS, Tripathi R, Khushu S, Kaushik S. Protocol error artifacts in MRI: sources and remedies revisited. Radiography. 2007;13(4):291–306.

    Article  Google Scholar 

  28. Kim SY, Park SH, Wu EH, Wang ZJ, Hope TA, Chang WC, Yeh BM. Transient respiratory motion artifact during arterial phase MRI with gadoxetate disodium: risk factor analyses. Am J Roentgenol. 2015;204(6):1220–7.

    Article  Google Scholar 

  29. Kochunov P, Lancaster JL, Glahn DC, Purdy D, Laird AR, Gao F, Fox P. Retrospective motion correction protocol for high-resolution anatomical MRI. Hum Brain Map. 2006;27(12):957–62.

    Article  Google Scholar 

  30. Krupa K, Bekiesińska-Figatowska M. Artifacts in magnetic resonance imaging. Pol J Radiol. 2015;80:93.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kubicek J, Strycek M, Cerny M, Penhaker M, Prokop O, Vilimek D. Quantitative and comparative analysis of effectivity and robustness for enhanced and optimized non-local mean filter combining pixel and patch information on mr images of musculoskeletal system. Sensors. 2021;21(12):4161.

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  32. Küstner T, Liebgott A, Mauch L, Martirosian P, Bamberg F, Nikolaou K, Yang B, Schick F, Gatidis S. Automated reference-free detection of motion artifacts in magnetic resonance images. Magn Reson Mater Phys Biol Med. 2018;31(2):243–56.

    Article  Google Scholar 

  33. Kuzmina E, Razumov A, Rogov OY, Adalsteinsson E, White J, Dylov DV. Autofocusing+: noise-resilient motion correction in magnetic resonance imaging. In: International conference on medical image computing and computer-assisted intervention. Springer; 2022. pp. 365–375.

  34. Lane BF, Vandermeer FQ, Oz RC, Irwin EW, McMillan AB, Wong-You-Cheong JJ. Comparison of sagittal T2-weighted blade and fast spin-echo MRI of the female pelvis for motion artifact and lesion detection. Am J Roentgenol. 2011;197(2):W307–13.

    Article  Google Scholar 

  35. Liau J, Lee J, Schroeder ME, Sirlin CB, Bydder M. Cardiac motion in diffusion-weighted MRI of the liver: artifact and a method of correction. J Magn Reson Imaging. 2012;35(2):318–27.

    Article  PubMed  Google Scholar 

  36. Lim A, Lo J, Wagner MW, Ertl-Wagner B, Sussman D. Motion artifact correction in fetal MRI based on a generative adversarial network method. Biomed Signal Process Control. 2023;81: 104484.

    Article  Google Scholar 

  37. Liu J, Kocak M, Supanich M, Deng J. Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB). Magn Reson Imaging. 2020;71:69–79.

    Article  PubMed  Google Scholar 

  38. Lorch B, Vaillant G, Baumgartner C, Bai W, Rueckert D, Maier A. Automated detection of motion artefacts in MR imaging using decision forests. J Med Eng. 2017;2017:4501647.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lyu Q, Shan H, Xie Y, Kwan AC, Otaki Y, Kuronuma K, Li D, Wang G. Cine cardiac MRI motion artifact reduction using a recurrent neural network. IEEE Trans Med Imaging. 2021;40:2170–81.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Maclaren J, Herbst M, Speck O, Zaitsev M. Prospective motion correction in brain imaging: a review. Magn Reson Med. 2013;69(3):621–36.

    Article  PubMed  Google Scholar 

  41. Mirowitz SA. Motion artifact as a pitfall in diagnosis of meniscal tear on gradient reoriented MRI of the knee. J Comput Assist Tomogr. 1994;18(2):279–82.

    Article  CAS  PubMed  Google Scholar 

  42. Mohaldin AH, et al. MRI image motion blur correction. 2018.

  43. Morelli JN, Runge VM, Ai F, Attenberger U, Vu L, Schmeets SH, Nitz WR, Kirsch JE. An image-based approach to understanding the physics of MR artifacts. Radiographics. 2011;31(3):849–66.

    Article  PubMed  Google Scholar 

  44. Mucelli RP, Amezich W, Longo R, Ricci C. Artifacts in MRI: a proposal for classification based upon the encoding process. In: Frontiers in European radiology. Springer; 1993. pp. 187–211.

  45. Odille F, Cîndea N, Mandry D, Pasquier C, Vuissoz PA, Felblinger J. Generalized MRI reconstruction including elastic physiological motion and coil sensitivity encoding. Magn Reson Med. 2008;59(6):1401–11.

    Article  PubMed  Google Scholar 

  46. Oh G, Lee JE, Ye JC. Unsupervised MR motion artifact deep learning using outlier-rejecting bootstrap aggregation. 2020. arXiv preprint arXiv:2011.06337.

  47. Oksuz I. Brain MRI artefact detection and correction using convolutional neural networks. Comput Methods Progr Biomed. 2021;199: 105909.

    Article  Google Scholar 

  48. Paling MR, Brookeman JR. Respiration artifacts in MR imaging: reduction by breath holding. J Comput Assist Tomogr. 1986;10(6):1080–2.

    Article  CAS  PubMed  Google Scholar 

  49. Pattany PM, Phillips JJ, Chiu LC, Lipcamon JD, Duerk JL, McNally JM, Mohapatra SN. Motion artifact suppression technique (MAST) for MR imaging. J Comput Assist Tomogr. 1987;11(3):369–77.

    Article  CAS  PubMed  Google Scholar 

  50. Peh WC, Chan JH. Artifacts in musculoskeletal magnetic resonance imaging: identification and correction. Skelet Radiol. 2001;30(4):179–91.

    Article  CAS  Google Scholar 

  51. Pipe JG. Motion correction with propeller MRI: application to head motion and free-breathing cardiac imaging. Magn Reson Med. 1999;42(5):963–9.

    Article  CAS  PubMed  Google Scholar 

  52. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to carp. Neuroimage. 2013;76:439–41.

    Article  PubMed  Google Scholar 

  53. Rohlfing T, Maurer CR Jr, O’Dell WG, Zhong J. Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images. Med Phys. 2004;31(3):427–32.

    Article  PubMed  Google Scholar 

  54. Rotman M, Brada R, Beniaminy I, Ahn S, Hardy CJ, Wolf L. Correcting motion artifacts in MRI scans using a deep neural network with automatic motion timing detection. In: Medical imaging 2021: physics of medical imaging, vol. 11595. International Society for Optics and Photonics; 2021. p. 1159514.

  55. Runge V, Clanton J, Partain C, James A Jr. Respiratory gating in magnetic resonance imaging at 0.5 Tesla. Radiology. 1984;151(2):521–3.

    Article  CAS  PubMed  Google Scholar 

  56. Safari M, Fatemi A, Archambault L. MAUDGAN: motion artifact unsupervised disentanglement generative adversarial network of multicenter MRI data with different brain tumors. medRxiv. 2023;2023–03.

  57. Sanchez-Panchuelo RM, Francis S, Bowtell R, Schluppeck D. Mapping human somatosensory cortex in individual subjects with 7 T functional MRI. J Neurophysiol. 2010;103(5):2544–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Sánchez-Panchuelo RM, Francis ST, Schluppeck D, Bowtell RW. Correspondence of human visual areas identified using functional and anatomical MRI in vivo at 7 T. J Magn Reson Imaging. 2012;35(2):287–99.

    Article  PubMed  Google Scholar 

  59. Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE, et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage. 2013;64:240–56.

    Article  PubMed  Google Scholar 

  60. Scarff CJ, Reynolds A, Goodyear BG, Ponton CW, Dort JC, Eggermont JJ. Simultaneous 3-T fMRI and high-density recording of human auditory evoked potentials. Neuroimage. 2004;23(3):1129–42.

    Article  PubMed  Google Scholar 

  61. Sciarra A, Chatterjee S, Dünnwald M, Placidi G, Nürnberger A, Speck O, Oeltze-Jafra S. Reference-less SSIM regression for detection and quantification of motion artefacts in brain MRIS. In: Medical imaging with deep learning. 2022.

  62. Shaw R, Sudre C, Ourselin S, Cardoso MJ. MRI k-space motion artefact augmentation: model robustness and task-specific uncertainty. 2018.

  63. Singh KR, Sharma A, Singh GK. Attention-guided residual W-Net for supervised cardiac magnetic resonance imaging segmentation. Biomed Signal Process Control. 2023;86: 105177.

    Article  Google Scholar 

  64. Smith TB. MRI artifacts and correction strategies. Imaging Med. 2010;2(4):445.

    Article  Google Scholar 

  65. Softways, M.I.G.: MRI artifacts—flow artifact. 2018. https://www.mr-tip.com/serv1.php?type=art &sub=Flow+Artifact.

  66. Sommer K, Saalbach A, Brosch T, Hall C, Cross N, Andre J. Correction of motion artifacts using a multiscale fully convolutional neural network. Am J Neuroradiol. 2020;41(3):416–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Spieker V, Eichhorn H, Hammernik K, Rueckert D, Preibisch C, Karampinos DC, Schnabel JA: Deep learning for retrospective motion correction in MRI: a comprehensive review. 2023. arXiv preprint arXiv:2305.06739.

  68. Stadler A, Schima W, Ba-Ssalamah A, Kettenbach J, Eisenhuber E. Artifacts in body MR imaging: their appearance and how to eliminate them. Eur Radiol. 2007;17(5):1242–55.

    Article  PubMed  Google Scholar 

  69. Tamada D, Kromrey ML, Ichikawa S, Onishi H, Motosugi U. Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MR imaging of the liver. Magn Reson Med Sci. 2020;19(1):64.

    Article  CAS  PubMed  Google Scholar 

  70. Tan J, Mougenot C, Pichardo S, Drake JM, Waspe AC. Motion compensation using principal component analysis and projection onto dipole fields for abdominal magnetic resonance thermometry. Magn Reson Med. 2019;81(1):195–207.

    Article  PubMed  Google Scholar 

  71. Teitelbaum G, Bradley W Jr, Klein B. MR imaging artifacts, ferromagnetism, and magnetic torque of intravascular filters, stents, and coils. Radiology. 1988;166(3):657–64.

    Article  CAS  PubMed  Google Scholar 

  72. Themes U. Truncation artifacts. 2016. https://radiologykey.com/truncation-artifacts/.

  73. Tripathi V, Tibdewal M, Mishra R. Comparison of different denoising networks on motion artifacted MRI scans. In: Machine intelligence techniques for data analysis and signal processing: proceedings of the 4th international conference MISP 2022, vol. 1. Springer; 2023. pp. 301–314.

  74. Tripathi V, Tibdewal MN, Mishra R. Modified restricted Boltzmann machine (MRBM) for denoising of motion artifact-induced MRI scans. Res Biomed Eng. 2023;39(1):73–83.

    Article  Google Scholar 

  75. Vakli P, Weiss B, Szalma J, Barsi P, Gyuricza I, Kemenczky P, Somogyi E, Nárai Á, Gál V, Hermann P, et al. Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics. Med Image Anal. 2023;88: 102850.

    Article  PubMed  Google Scholar 

  76. Vargas M, Delavelle J, Kohler R, Becker C, Lovblad K. Brain and spine MRI artifacts at 3 Tesla. J Neuroradiol. 2009;36(2):74–81.

    Article  CAS  PubMed  Google Scholar 

  77. White M, Hawkes D, Melbourne A, Collins D, Coolens C, Hawkins M, Leach M, Atkinson D. Motion artifact correction in free-breathing abdominal MRI using overlapping partial samples to recover image deformations. Magn Reson Med. 2009;62(2):440–9.

    Article  CAS  PubMed  Google Scholar 

  78. Wood ML, Henkelman RM. Suppression of respiratory motion artifacts in magnetic resonance imaging. Med Phys. 1986;13(6):794–805.

    Article  CAS  PubMed  Google Scholar 

  79. Yang RK, Roth CG, Ward RJ, deJesus JO, Mitchell DG. Optimizing abdominal MR imaging: approaches to common problems. Radiographics. 2010;30(1):185–99.

    Article  PubMed  Google Scholar 

  80. Zanin M, Aitya NA, Basilio J, Baumbach J, Benis A, Behera CK, Bucholc M, Castiglione F, Chouvarda I, Comte B, et al. An early stage researcher’s primer on systems medicine terminology. Netw Syst Med. 2021;4(1):2–50.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Zhou SK, Le HN, Luu K, Nguyen HV, Ayache N. Deep reinforcement learning in medical imaging: a literature review. Medi Image Anal. 2021;73: 102193.

    Article  Google Scholar 

  82. Zhuo J, Gullapalli RP. MR artifacts, safety, and quality control. Radiographics. 2006;26(1):275–97.

    Article  PubMed  Google Scholar 

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  1. G. H. Raisoni University, Amravati, 444701, India

    Vijay R. Tripathi, Manish N. Tibdewal & Ravi Mishra

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  1. Vijay R. Tripathi
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Contributions

Conceptualization was done by VRT, MNT, and RM. All the literature reading and data gathering were performed by VRT. The formal analysis was performed by VRT. Manuscript writing- original draft preparation was done by VRT. Review and editing was done by MNT and RM. Visualization work was carried out by VRT, MNT and RM.

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Correspondence to Vijay R. Tripathi.

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Tripathi, V.R., Tibdewal, M.N. & Mishra, R. A survey on Motion Artifact Correction in Magnetic Resonance Imaging for Improved Diagnostics. SN COMPUT. SCI. 5, 281 (2024). https://doi.org/10.1007/s42979-023-02596-1

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