J Korean Soc Magn Reson Med.  2013 Dec;17(4):275-285. 10.13104/jksmrm.2013.17.4.275.

Gaussian Filtering Effects on Brain Tissue-masked Susceptibility Weighted Images to Optimize Voxel-based Analysis

• Hwang EJ
• Kim MJ
• Jahng GH

• KMID: 2144329
• DOI: http://doi.org/10.13104/jksmrm.2013.17.4.275

Abstract

PURPOSE
The objective of this study was to investigate effects of different smoothing kernel sizes on brain tissue-masked susceptibility-weighted images (SWI) obtained from normal elderly subjects using voxel-based analyses.
MATERIALS AND METHODS
Twenty healthy human volunteers (mean age+/-SD = 67.8 +/- 6.09 years, 14 females and 6 males) were studied after informed consent. A fully first-order flow-compensated three-dimensional (3D) gradient-echo sequence ran to obtain axial magnitude and phase images to generate SWI data. In addition, sagittal 3D T1-weighted images were acquired with the magnetization-prepared rapid acquisition of gradient-echo sequence for brain tissue segmentation and imaging registration. Both paramagnetically (PSWI) and diamagnetically (NSWI) phase-masked SWI data were obtained with masking out non-brain tissues. Finally, both tissue-masked PSWI and NSWI data were smoothed using different smoothing kernel sizes that were isotropic 0, 2, 4, and 8 mm Gaussian kernels. The voxel-based comparisons were performed using a paired t-test between PSWI and NSWI for each smoothing kernel size.
RESULTS
The significance of comparisons increased with increasing smoothing kernel sizes. Signals from NSWI were greater than those from PSWI. The smoothing kernel size of four was optimal to use voxel-based comparisons. The bilaterally different areas were found on multiple brain regions.
CONCLUSION
The paramagnetic (positive) phase mask led to reduce signals from high susceptibility areas. To minimize partial volume effects and contributions of large vessels, the voxel-based analysis on SWI with masked non-brain components should be utilized.

Keyword

Susceptibility weighted imaging; Phase mask; Brain tissue-mask; Smoothing kernel size; Voxel-wise analysis

MeSH Terms

Aged
Brain*
Female
Healthy Volunteers
Humans
Informed Consent
Masks

Figure

• 

  Fig. 1 The representative brain tissue-masked images a. magnitude image b. phase image c. positively phase-masked susceptibility weighted image with 4 phase mask multiplications (PSWI4) with a smoothing factor of 4 and d. negatively phase-masked susceptibility weighted image with 4 phase mask multiplications (NSWI4) with a smoothing factor of 4.

• 

  Fig. 2 Result of voxel-wise comparisons between positively phase-masked susceptibility weighted images (PSWI4) and negatively phase-masked susceptibility images (NSWI4) with smoothing factors of 0, 2, 4 and 8. The highlighted red regions show the areas where NSWI4 > PSWI4 (FWE, p = 0.005), and the highlighted blue regions show the areas where PSWI4 > NSWI4 (FWE, p = 0.005).

Reference

1. Rauscher A, Sedlacik J, Barth M, Mentzel HJ, Reichenbach JR. Magnetic susceptibility-weighted MR phase imaging of the human brain. AJNR Am J Neuroradiol. 2005; 26:736–742.

2. Haacke EM, Cheng NY, House MJ, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging. 2005; 23:1–25.

3. Haacke EM, Mittal S, Wu Z, Neelavalli J, Cheng YC. Susceptibility-weighted imaging: technical aspects and clinical applications, part 1. AJNR Am J Neuroradiol. 2009; 30:19–30.

4. Hagberg GE, Welch EB, Greiser A. The sign convention for phase values on different vendor systems: definition and implications for susceptibility-weighted imaging. Magn Reson Imaging. 2010; 28:297–300.

5. Ashburner J, Friston KJ. Voxel-based morphometry--the methods. Neuroimage. 2000; 11:805–821.

6. Eissa A, Lebel RM, Korzan JR, et al. Detecting lesions in multiple sclerosis at 4.7 tesla using phase susceptibility-weighting and t2-weighting. J Magn Reson Imaging. 2009; 30:737–742.

7. Grabner G, Dal-Bianco A, Schernthaner M, Vass K, Lassmann H, Trattnig S. Analysis of multiple sclerosis lesions using a fusion of 3.0 t flair and 7.0 t swi phase: flair swi. J Magn Reson Imaging. 2011; 33:543–549.

8. Kim MJ, Jahng GH, Lee HY, et al. Development of a Korean standard structural brain template in cognitive normals and patients with mild cognitive impairment and Alzheimer's disease. J Korean Soc Magn Reson Med. 2010; 14:103–114.

9. Fukunaga M, Li TQ, van Gelderen P, et al. Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast. Proc Natl Acad Sci U S A. 2010; 107:3834–3839.

10. Duyn JH, van Gelderen P, Li TQ, de Zwart JA, Koretsky AP, Fukunaga M. High-field MRI of brain cortical substructure based on signal phase. Proc Natl Acad Sci U S A. 2007; 104:11796–11801.

11. 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.

12. Niwa T, Aida N, Kawaguchi H, et al. Anatomic dependency of phase shifts in the cerebral venous system of neonates at susceptibility-weighted MRI. J Magn Reson Imaging. 2011; 34:1031–1036.

13. Schäfer A, Wharton S, Gowland P, Bowtell R. Using magnetic field simulation to study susceptibility-related phase contrast in gradient echo MRI. Neuroimage. 2009; 48:126–137.

14. Shmueli K, de Zwart JA, van Gelderen P, Li TQ, Dodd SJ, Duyn JH. Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data. Magn Reson Med. 2009; 62:1510–1522.

15. de Rochefort L, Brown R, Prince MR, Wang Y. Quantitative MR susceptibility mapping using piece-wise constant regularized inversion of the magnetic field. Magn Reson Med. 2008; 60:1003–1009.

16. Liu T, Spincemaille P, de Rochefort L, Kressler B, Wang Y. Calculation of susceptibility through multiple orientation sampling (cosmos): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn Reson Med. 2009; 61:196–204.