J Korean Soc Magn Reson Med.  2012 Apr;16(1):6-15. 10.13104/jksmrm.2012.16.1.6.

Background Gradient Correction using Excitation Pulse Profile for Fat and T2* Quantification in 2D Multi-Slice Liver Imaging

Affiliations
  • 1Department of Electrical and Electronic Engineering, Yonsei University, Korea. donghyunkim@yonsei.ac.kr
  • 2Department of Radiology, Yonsei University, Korea.

Abstract

PURPOSE
The objective of this study was to develop background gradient correction method using excitation pulse profile compensation for accurate fat and T2* quantification in the liver.
MATERIALS AND METHODS
In liver imaging using gradient echo, signal decay induced by linear background gradient is weighted by an excitation pulse profile and therefore hinders accurate quantification of T2* and fat. To correct this, a linear background gradient in the slice-selection direction was estimated from a B0 field map and signal decays were corrected using the excitation pulse profile. Improved estimation of fat fraction and T2* from the corrected data were demonstrated by phantom and in vivo experiments at 3 Tesla magnetic field.
RESULTS
After correction, in the phantom experiments, the estimated T2* and fat fractions were changed close to that of a well-shimmed condition while, for in vivo experiments, the background gradients were estimated to be up to approximately 120 microT/m with increased homogeneity in T2* and fat fractions obtained.
CONCLUSION
The background gradient correction method using excitation pulse profile can reduce the effect of macroscopic field inhomogeneity in signal decay and can be applied for simultaneous fat and iron quantification in 2D gradient echo liver imaging.

Keyword

Fat quantification; T2* measurement; IDEAL; field inhomogeneity; pulse profile; liver imaging

MeSH Terms

Compensation and Redress
Iron
Liver
Magnetics
Magnets
Iron

Reference

1. Hussain HK, Chenevert TL, Londy FJ, et al. Hepatic fat fraction: MR imaging for quantitative measurement and display--early experience. Radiology. 2005. 237:1048–1055.
2. Park H, Cho H, Kim E, Hur G, Kim Y, Lee B. Detection of hepatic lesion: comparison of free-breathing and respiratory-triggered diffusion-weighted MR imaging on 1.5-T MR system. J Korean Soc Magn Reson Med. 2011. 15:22–31.
3. Dixon WT. Simple proton spectroscopic imaging. Radiology. 1984. 153:189–194.
4. Glover GH, Schneider E. Three-point Dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magn Reson Med. 1991. 18:371–383.
5. Wood JC, Enriquez C, Ghugre N, et al. MRI R2 and R2* mapping accurately estimates hepatic iron concentration in transfusion-dependent thalassemia and sickle cell disease patients. Blood. 2005. 106:1460–1465.
6. O'Regan DP, Callaghan MF, Wylezinska-Arridge M, et al. Liver fat content and T2*: simultaneous measurement by using breath-hold multiecho MR imaging at 3.0 T--feasibility. Radiology. 2008. 247:550–557.
7. Reeder SB, Pineda AR, Wen Z, et al. Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): application with fast spin-echo imaging. Magn Reson Med. 2005. 54:636–644.
8. Yu H, McKenzie CA, Shimakawa A, et al. Multiecho reconstruction for simultaneous water-fat decomposition and T2* estimation. J Magn Reson Imaging. 2007. 26:1153–1161.
9. Haacke EM, Tkach JA, Parrish TB. Reduction of T2* dephasing in gradient field-echo imaging. Radiology. 1989. 170:457–462.
10. Cho S, Kim P, Lim J, Ahn C. Model-based Gradient Compensation in Spiral Imaging. J Korean Soc Magn Reson Med. 2009. 13:15–21.
11. Cho ZH, Ro YM. Reduction of susceptibility artifact in gradient-echo imaging. Magn Reson Med. 1992. 23:193–200.
12. Fernandez-Seara MA, Wehrli FW. Postprocessing technique to correct for background gradients in image-based R*(2) measurements. Magn Reson Med. 2000. 44:358–366.
13. Dahnke H, Schaeffter T. Limits of detection of SPIO at 3.0 T using T2 relaxometry. Magn Reson Med. 2005. 53:1202–1206.
14. Pauly JM. New approach to selective excitation for magnetic resonance imaging. 1990. Stanford University;Doctral Thesis.
15. Chebrolu VV, Hines CD, Yu H, et al. Independent estimation of T*2 for water and fat for improved accuracy of fat quantification. Magn Reson Med. 2010. 63:849–857.
16. Yu H, Reeder SB, Shimakawa A, Brittain JH, Pelc NJ. Field map estimation with a region growing scheme for iterative 3-point water-fat decomposition. Magn Reson Med. 2005. 54:1032–1039.
17. Jenkinson M. Fast, automated, N-dimensional phase-unwrapping algorithm. Magn Reson Med. 2003. 49:193–197.
18. Lu W, Hargreaves BA. Multiresolution field map estimation using golden section search for water-fat separation. Magn Reson Med. 2008. 60:236–244.
19. Bydder M, Shiehmorteza M, Yokoo T, et al. Assessment of liver fat quantification in the presence of iron. Magn Reson Imaging. 2010. 28:767–776.
Full Text Links
  • JKSMRM
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr