Investig Magn Reson Imaging.  2018 Dec;22(4):209-217. 10.13104/imri.2018.22.4.209.

Susceptibility Weighted Imaging of the Cervical Spinal Cord with Compensation of Respiratory-Induced Artifact

Affiliations
  • 1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. donghyunkim@yonsei.ac.kr
  • 2Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • 3Department of Radiology, Gachon University Gil Medical Center, Incheon, Korea.

Abstract

PURPOSE
The objective of this study was to obtain improved susceptibility weighted images (SWI) of the cervical spinal cord using respiratory-induced artifact compensation.
MATERIALS AND METHODS
The artifact from B0 fluctuations by respiration could be compensated using a double navigator echo approach. The two navigators were inserted in an SWI sequence before and after the image readouts. The B0 fluctuation was measured by each navigator echoes, and the inverse of the fluctuation was applied to eliminate the artifact from fluctuation. The degree of compensation was quantified using a quality index (QI) term for compensated imaging using each navigator. Also, the effect of compensation was analyzed according to the position of the spinal cord using QI values.
RESULTS
Compensation using navigator echo gave the improved visualization of SWI in cervical spinal cord compared to non-compensated images. Before compensation, images were influenced by artificial noise from motion in both the superior (QI = 0.031) and inferior (QI = 0.043) regions. In most parts of the superior regions, the second navigator resulted in better quality (QI = 0.024, P < 0.01) compared to the first navigator, but in the inferior regions the first navigator showed better quality (QI = 0.033, P < 0.01) after correction.
CONCLUSION
Motion compensation using a double navigator method can increase the improvement of the SWI in the cervical spinal cord. The proposed method makes SWI a useful tool for the diagnosis of spinal cord injury by reducing respiratory-induced artifact.

Keyword

SWI; Cervical spinal cord; Motion compensation; Respiratory-induced artifact; Navigator echo

MeSH Terms

Artifacts*
Cervical Cord*
Compensation and Redress*
Diagnosis
Methods
Noise
Qi
Respiration
Spinal Cord
Spinal Cord Injuries

Figure

  • Fig. 1 Pulse sequence diagram including two navigator echoes and imaging echoes.

  • Fig. 2 GRE images and phase of the navigator signal from representative slices. The value shown in the image means QI. The 12th slice (top, C3 level) shows the good result when compensation using the second navigator echo data is used. The 10th slice (middle, C4 level) shows similar compensation between the first and second navigator, which is supported from the plot of the phase navigator signal (right). The 5th slice (bottom, C6 level) shows better compensation when the first navigator data is used for correction.

  • Fig. 3 Representative magnitude, phase, and SWI showing (a) before compensation and (b) after compensation of respiratory-induced artifact. The blue boxes indicate better corrected images when using the first navigator, and the red boxes indicate better corrected images when using the second navigator as determined by the QI factors.

  • Fig. 4 Comparison of the QI value for the (a) superior part (C1–C4) and (b) inferior part (C5–C7) of the cervical spinal cord from the 5 volunteers. Top and bottom of each box are the 25th and 75th percentiles of the samples, respectively. The line in the middle of each box represents the sample median, and the cross represents the mean value. The whisker line shows adjacent values. Asterisks indicate P < 0.005.

  • Fig. 5 GRE images (a, c) and SWI (b, d) of uncorrected and corrected images using the proposed respiratory noise compensation method. (a, b) Tumor patient and (c, d) hemorrhagic patient. The yellow arrows indicate microhemorrhage or vascular structures containing deoxyhemoglobin. The red arrows indicate hemorrhage lesion.


Reference

1. Thomas B, Somasundaram S, Thamburaj K, et al. Clinical applications of susceptibility weighted MR imaging of the brain - a pictorial review. Neuroradiology. 2008; 50:105–116.
Article
2. Wycliffe ND, Choe J, Holshouser B, Oyoyo UE, Haacke EM, Kido DK. Reliability in detection of hemorrhage in acute stroke by a new three-dimensional gradient recalled echo susceptibility-weighted imaging technique compared to computed tomography: a retrospective study. J Magn Reson Imaging. 2004; 20:372–377.
Article
3. Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med. 2004; 52:612–618.
Article
4. Wang M, Dai Y, Han Y, Haacke EM, Dai J, Shi D. Susceptibility weighted imaging in detecting hemorrhage in acute cervical spinal cord injury. Magn Reson Imaging. 2011; 29:365–373.
Article
5. Mittal S, Wu Z, Neelavalli J, Haacke EM. Susceptibility-weighted imaging: technical aspects and clinical applications, part 2. AJNR Am J Neuroradiol. 2009; 30:232–252.
Article
6. Ishizaka K, Kudo K, Fujima N, et al. Detection of normal spinal veins by using susceptibility-weighted imaging. J Magn Reson Imaging. 2010; 31:32–38.
Article
7. Biber S, Wohlfarth K, Kirsch J, Schmidt A. Design of a local shim coil to improve B0 homogeneity in the cervical spine region. In : Proceedings of the 20th Annual Meeting of ISMRM; Melbourne, Australia. 2012. p. 2746.
8. Hock A, Fuchs A, Boesiger P, Kollias SS, Henning A. Electrocardiogram-triggered, higher order, projection-based B(0) shimming allows for fast and reproducible shim convergence in spinal cord (1)H MRS. NMR Biomed. 2013; 26:329–335.
9. Stroman PW, Wheeler-Kingshott C, Bacon M, et al. The current state-of-the-art of spinal cord imaging: methods. Neuroimage. 2014; 84:1070–1081.
Article
10. Verma T, Cohen-Adad J. Effect of respiration on the B0 field in the human spinal cord at 3T. Magn Reson Med. 2014; 72:1629–1636.
11. Holland AE, Goldfarb JW, Edelman RR. Diaphragmatic and cardiac motion during suspended breathing: preliminary experience and implications for breath-hold MR imaging. Radiology. 1998; 209:483–489.
Article
12. Ehman RL, McNamara MT, Pallack M, Hricak H, Higgins CB. Magnetic resonance imaging with respiratory gating: techniques and advantages. AJR Am J Roentgenol. 1984; 143:1175–1182.
Article
13. Anderson AW, Gore JC. Analysis and correction of motion artifacts in diffusion weighted imaging. Magn Reson Med. 1994; 32:379–387.
Article
14. Durand E, van de Moortele PF, Pachot-Clouard M, Le Bihan D. Artifact due to B(0) fluctuations in fMRI: correction using the k-space central line. Magn Reson Med. 2001; 46:198–201.
15. Butts K, de Crespigny A, Pauly JM, Moseley M. Diffusion-weighted interleaved echo-planar imaging with a pair of orthogonal navigator echoes. Magn Reson Med. 1996; 35:763–770.
Article
16. Song R, Tipirneni A, Johnson P, Loeffler RB, Hillenbrand CM. Evaluation of respiratory liver and kidney movements for MRI navigator gating. J Magn Reson Imaging. 2011; 33:143–148.
Article
17. Wen J, Cross AH, Yablonskiy DA. On the role of physiological fluctuations in quantitative gradient echo MRI: implications for GEPCI, QSM, and SWI. Magn Reson Med. 2015; 73:195–203.
Article
18. Fu ZW, Wang Y, Grimm RC, et al. Orbital navigator echoes for motion measurements in magnetic resonance imaging. Magn Reson Med. 1995; 34:746–753.
Article
19. Nam Y, Kim DH, Lee J. Physiological noise compensation in gradient-echo myelin water imaging. Neuroimage. 2015; 120:345–349.
Article
20. Lee J, Santos JM, Conolly SM, Miller KL, Hargreaves BA, Pauly JM. Respiration-induced B0 field fluctuation compensation in balanced SSFP: real-time approach for transition-band SSFP fMRI. Magn Reson Med. 2006; 55:1197–1201.
Article
21. Lee H, Nam Y, Han D, Gho S-M, Kim D-H. SWI of the cervical-spinal cord with respiration noise correction using navigator echo. In : Proceedings of the 23rd Annual Meeting of ISMRM; Toronto, Ontario, Canada. 2015. p. 1727.
22. Balu N, Chu B, Hatsukami TS, Yuan C, Yarnykh VL. Comparison between 2D and 3D high-resolution black-blood techniques for carotid artery wall imaging in clinically significant atherosclerosis. J Magn Reson Imaging. 2008; 27:918–924.
Article
23. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med. 2002; 47:1202–1210.
Article
24. Jang U, Nam Y, Kim DH, Hwang D. Improvement of the SNR and resolution of susceptibility-weighted venography by model-based multi-echo denoising. Neuroimage. 2013; 70:308–316.
Article
25. Kaufman L, Kramer DM, Crooks LE, Ortendahl DA. Measuring signal-to-noise ratios in MR imaging. Radiology. 1989; 173:265–267.
Article
26. McGee KP, Manduca A, Felmlee JP, Riederer SJ, Ehman RL. Image metric-based correction (autocorrection) of motion effects: analysis of image metrics. J Magn Reson Imaging. 2000; 11:174–181.
Article
27. Mortamet B, Bernstein MA, Jack CR Jr, et al. Automatic quality assessment in structural brain magnetic resonance imaging. Magn Reson Med. 2009; 62:365–372.
Article
28. Hu X, Kim SG. Reduction of signal fluctuation in functional MRI using navigator echoes. Magn Reson Med. 1994; 31:495–503.
Article
29. Wowk B, McIntyre MC, Saunders JK. k-Space detection and correction of physiological artifacts in fMRI. Magn Reson Med. 1997; 38:1029–1034.
Article
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