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Automated reference-free detection of motion artifacts in magnetic resonance images

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Abstract

Objectives

Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture.

Materials and methods

T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis.

Results

On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively.

Conclusion

Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

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References

  1. Ollier W, Sprosen T, Peakman T (2005) UK Biobank: from concept to reality. Pharmacogenomics 6(6):639–646

    Article  PubMed  Google Scholar 

  2. Bamberg F, Kauczor HU, Weckbach S, Schlett CL, Forsting M, Ladd SC, Greiser KH, Weber MA, Schulz-Menger J, Niendorf T, Pischon T, Caspers S, Amunts K, Berger K, Bulow R, Hosten N, Hegenscheid K, Kroncke T, Linseisen J, Gunther M, Hirsch JG, Kohn A, Hendel T, Wichmann HE, Schmidt B, Jockel KH, Hoffmann W, Kaaks R, Reiser MF, Volzke H (2015) Whole-body MR imaging in the German National Cohort: rationale, design, and technical background. Radiology 277(1):206–220

    Article  PubMed  Google Scholar 

  3. De Wilde JP, Lunt JA, Straughan K (1997) Information in magnetic resonance images: evaluation of signal, noise and contrast. Med Biol Eng Comput 35(3):259–265

    Article  PubMed  Google Scholar 

  4. Eck BL, Fahmi R, Brown KM, Zabic S, Raihani N, Miao J, Wilson DL (2015) Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction. Med Phys 42(10):6098

    Article  PubMed  PubMed Central  Google Scholar 

  5. Sinha N, Ramakrishnan AG (2010) Quality assessment in magnetic resonance images. Crit Rev Biomed Eng 38(2):127–141

    Article  PubMed  Google Scholar 

  6. Pizarro RA, Cheng X, Barnett A, Lemaitre H, Verchinski BA, Goldman AL, Xiao E, Luo Q, Berman KF, Callicott JH, Weinberger DR, Mattay VS (2016) Automated quality assessment of structural magnetic resonance brain images based on a supervised machine learning algorithm. Front Neurosci 10:52

    Google Scholar 

  7. Küstner T, Bahar P, Würslin C, Gatidis S, Martirosian P, Schwenzer NF, Yang B, Schmidt H (2015)A new approach for automatic image quality assessment. In: Proceedings of the 23rd scientific meeting, International Society for Magnetic Resonance in Medicine, Toronto, Canada. p 4735

  8. Deng L, Yu D (2014) Deep learning: methods and applications. Found Trend Signal Process 7(3–4):197–387

    Article  Google Scholar 

  9. Cheng JY, Zhang T, Ruangwattanapaisarn N, Alley MT, Uecker M, Pauly JM, Lustig M, Vasanawala SS (2015) Free-breathing pediatric MRI with nonrigid motion correction and acceleration. J Magn Reson Imaging 42(2):407–420

    Article  PubMed  Google Scholar 

  10. Cruz G, Atkinson D, Buerger C, Schaeffter T, Prieto C (2016) Accelerated motion corrected three-dimensional abdominal MRI using total variation regularized SENSE reconstruction. Magn Reson Med 75(4):1484–1498

    Article  PubMed  Google Scholar 

  11. Feng L, Grimm R, Block KT, Chandarana H, Kim S, Xu J, Axel L, Sodickson DK, Otazo R (2014) Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med 72(3):707–717

    Article  PubMed  Google Scholar 

  12. Küstner T, Würslin C, Schwartz M, Martirosian P, Gatidis S, Brendle C, Seith F, Schick F, Schwenzer NF, Yang B, Schmidt H (2016) Self-navigated 4D cartesian imaging of periodic motion in the body trunk using partial k-space compressed sensing. Magn Reson Med. doi:10.1002/mrm.26406

    PubMed  Google Scholar 

  13. Rank CM, Heusser T, Buzan MT, Wetscherek A, Freitag MT, Dinkel J, Kachelriess M (2016) 4D respiratory motion-compensated image reconstruction of free-breathing radial MR data with very high undersampling. Magn Reson Med 77(3):1170–1183

    Article  PubMed  Google Scholar 

  14. Zhu Y, Guo Y, Lingala SG, Marc Lebel R, Law M, Nayak KS (2015) GOCART: GOlden-angle CArtesian randomized time-resolved 3D MRI. J Magn Reson Imaging 34(7):940–950

    Article  Google Scholar 

  15. Godenschweger F, Kagebein U, Stucht D, Yarach U, Sciarra A, Yakupov R, Lusebrink F, Schulze P, Speck O (2016) Motion correction in MRI of the brain. Phys Med Biol 61(5):R32–R56

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Maclaren J, Herbst M, Speck O, Zaitsev M (2013) Prospective motion correction in brain imaging: a review. Magn Reson Med 69(3):621–636

    Article  PubMed  Google Scholar 

  17. Zaitsev M, Maclaren J, Herbst M (2015) Motion artifacts in MRI: a complex problem with many partial solutions. J Magn Reson Imaging 42(4):887–901

    Article  PubMed  PubMed Central  Google Scholar 

  18. Baum EB, Haussler D (1989) What size net gives valid generalization? Neural Comput 1(1):151–160

    Article  Google Scholar 

  19. Kingma DP, Ba J (2014) Adam: a Method for stochastic optimization. Comput Res Repos 1412.6980

  20. Chollet F (2015) KERAS. https://github.com/fchollet/keras

  21. Atkinson D, Hill DL, Stoyle PN, Summers PE, Keevil SF (1997) Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. IEEE Trans Med Imaging 16(6):903–910

    Article  CAS  PubMed  Google Scholar 

  22. Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H (2015) Understanding neural networks through deep visualization. In: Proceedings of the 31st International conference on machine learning, Deep Learning Workshop. Lille, pp 1–12

  23. Parikh N, Boyd S (2014) Proximal algorithms. Found Trend Optim 1(3):127–239

    Article  Google Scholar 

  24. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25. Curran Associates, Red Hook, pp 1097–1105

    Google Scholar 

  25. Ruder S (2016) An overview of gradient descent optimization algorithms. CoRR abs/1609.04747

  26. Aksoy M, Forman C, Straka M, Cukur T, Hornegger J, Bammer R (2012) Hybrid prospective and retrospective head motion correction to mitigate cross-calibration errors. Magn Reson Med 67(5):1237–1251

    Article  PubMed  Google Scholar 

  27. McGee KP, Manduca A, Felmlee JP, Riederer SJ, Ehman RL (2000) Image metric-based correction (autocorrection) of motion effects: analysis of image metrics. J Magn Reson Imaging 11(2):174–181

    Article  CAS  PubMed  Google Scholar 

  28. Gilliam C, Küstner T, Blu T (2016) 3D motion flow estimation using local all-pass filters. In: Proceedings of the 13th IEEE International Symposium on Biomedical Imaging, 13–16. pp 282–285

  29. Miao J, Huo D, Wilson DL (2008) Quantitative image quality evaluation of MR images using perceptual difference models. Med Phys 35(6):2541–2553

    Article  PubMed  PubMed Central  Google Scholar 

  30. Oh J, Woolley SI, Arvanitis TN, Townend JN (2001) A multistage perceptual quality assessment for compressed digital angiogram images. IEEE Trans Med Imaging 20(12):1352–1361

    Article  CAS  PubMed  Google Scholar 

  31. Ouled Zaid A, Fradj BB (2010) Coronary angiogram video compression for remote browsing and archiving applications. Comput Med Imaging Graph 34(8):632–641

    Article  PubMed  Google Scholar 

  32. Flask CA, Salem KA, Moriguchi H, Lewin JS, Wilson DL, Duerk JL (2003) Keyhole Dixon method for faster, perceptually equivalent fat suppression. J Magn Reson Imaging 18(1):103–112

    Article  PubMed  Google Scholar 

  33. Mortamet B, Bernstein MA, Jack CR Jr, Gunter JL, Ward C, Britson PJ, Meuli R, Thiran JP, Krueger G (2009) Automatic quality assessment in structural brain magnetic resonance imaging. Magn Reson Med 62(2):365–372

    Article  PubMed  PubMed Central  Google Scholar 

  34. Woodard JP, Carley-Spencer MP (2006) No-reference image quality metrics for structural MRI. Neuroinformatics 4(3):243–262

    Article  PubMed  Google Scholar 

  35. Tisdall MD, Atkins MS (2006) Using human and model performance to compare MRI reconstructions. IEEE Trans Med Imaging 25(11):1510–1517

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Stuttgart, Institute of Signal Processing and System Theory, Stuttgart, Germany

    Thomas Küstner, Annika Liebgott, Lukas Mauch & Bin Yang

  2. Department of Radiology, University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany

    Thomas Küstner, Annika Liebgott, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Fritz Schick & Sergios Gatidis

Authors
  1. Thomas Küstner
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  2. Annika Liebgott
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  3. Lukas Mauch
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  4. Petros Martirosian
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  5. Fabian Bamberg
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  6. Konstantin Nikolaou
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  7. Bin Yang
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  8. Fritz Schick
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  9. Sergios Gatidis
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Contributions

Küstner: protocol/project development, data collection or management, data analysis; Liebgott: data analysis; Mauch: data analysis; Martirosian: protocol/project development, data collection or management; Bamberg: data collection or management; Nikolaou: data collection or management; Yang: protocol/project development, data analysis; Schick: protocol/project development; and Gatidis: data collection or management, data analysis

Corresponding author

Correspondence to Thomas Küstner.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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This article does not contain any studies with animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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Küstner, T., Liebgott, A., Mauch, L. et al. Automated reference-free detection of motion artifacts in magnetic resonance images. Magn Reson Mater Phy 31, 243–256 (2018). https://doi.org/10.1007/s10334-017-0650-z

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  • DOI: https://doi.org/10.1007/s10334-017-0650-z

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