대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제41권1호
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  • Pages.28-34
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  • 2020
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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완전 데이터 적응형 MLS 근사 알고리즘을 이용한 Interleaved MRI의 움직임 보정 알고리즘

Motion Artifact Reduction Algorithm for Interleaved MRI using Fully Data Adaptive Moving Least Squares Approximation Algorithm

  • Nam, Haewon (Department of Liberal Arts, Hongik University)
  • 투고 : 2020.01.09
  • 심사 : 2020.02.13
  • 발행 : 2020.02.29
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초록

In this paper, we introduce motion artifact reduction algorithm for interleaved MRI using an advanced 3D approximation algorithm. The motion artifact framework of this paper is data corrected by post-processing with a new 3-D approximation algorithm which uses data structure for each voxel. In this study, we simulate and evaluate our algorithm using Shepp-Logan phantom and T1-MRI template for both scattered dataset and uniform dataset. We generated motion artifact using random generated motion parameters for the interleaved MRI. In simulation, we use image coregistration by SPM12 (https://www.fil.ion.ucl.ac.uk/spm/) to estimate the motion parameters. The motion artifact correction is done with using full dataset with estimated motion parameters, as well as use only one half of the full data which is the case when the half volume is corrupted by severe movement. We evaluate using numerical metrics and visualize error images.

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참고문헌

  1. Pipe JG. Motion Correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging. Magn Reson Med. 1999;42:963-9. https://doi.org/10.1002/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L
  2. Stuckey S, Goh TD et al. Hyperintensity in the subarachnoid space on FLAIR MRI. Neuroradiology. 2007;189(4):913-21.
  3. Ro YM, Yi J., Zho ZH. Intra-Motion Compensation Using CSRS method in MRI. J. of KOSOMBE. 1994;15(4):377-82.
  4. Welch EB., Felmlee JP. et al. Motion correction using the kspace phase difference of orthogonal acquisitions. Magn. Res. in Med. 2002;48:147-56. https://doi.org/10.1002/mrm.10179
  5. Nam H., Lee Y., Jeong B., Park HJ., Yoon J. Motion correction of magnetic resonance imaging data by using adaptive moving least squares method. Meg Res Imaging. 2015;33:659-70. https://doi.org/10.1016/j.mri.2015.02.003
  6. Kim B, Boes JL, et al. Motion correction in fMRI via registration of individual slice into an anatomical volume. Magn Reson Med. 1999;41(5):964-72. https://doi.org/10.1002/(SICI)1522-2594(199905)41:5<964::AID-MRM16>3.0.CO;2-D
  7. Levin D. The approximation power of moving least-squares. Math. Comp. 1998;67:1517-31. https://doi.org/10.1090/S0025-5718-98-00974-0
  8. Takeda H., Farsiu S., and Milanfar P. Kernel regression for image processing and reconstruction. IEEE Tran. Image. Proc. 2007;16(2):349-66. https://doi.org/10.1109/TIP.2006.888330
  9. Jang S, Nam H, Lee Y, Jeong B, Yoon J. Data adapted moving least squares method for application to 3-D image interpolation. Phys Med Biol. 2013;35(2):424-34.
  10. Rohlfing T, Rademacher MH, Pfefferbaum A. Volume reconstruction by inverse interpolation: application to interleaved MR motion correction. Med. Image Comput. Comput. Assist. Interv. 2008;1:798-806.
  11. Shepp LA, Logan BF. The Fourier reconstruction of a head section. IEEE Trans. on Nuclear Sciences. 1974.
  12. Bolan P. 3D Shepp-Logan Phantom. 2020; Available from: https://www.mathworks.com/matlabcentral/fileexchange/50974-3d-shepp-logan-phantom.