Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
권호 표지
DOI QR코드

DOI QR Code

ROI Study for Diffusion Tensor Image with Partial Volume Effect

부분용적효과를 고려한 확산텐서영상에 대한 관심영역 분석 연구

  • Choi, Woohyuk (Department of Biomedical Engineering, Catholic University of Daegu) ;
  • Yoon, Uicheul (Department of Biomedical Engineering, Catholic University of Daegu)
  • 최우혁 (대구가톨릭대학교 의공학과) ;
  • 윤의철 (대구가톨릭대학교 의공학과)
  • Received : 2016.02.29
  • Accepted : 2016.05.10
  • Published : 2016.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we proposed ameliorated method for region of interest (ROI) study to improve its accuracy using partial volume effect (PVE). PVE which arose in volumetric images when more than one tissue type occur in a voxel, could be used to reduce an amount of gray matter and cerebrospinal fluid within ROI of diffusion tensor image (DTI). In order to define ROIs, individual b0 image was spatially aligned to the JHU DTI-based atlas using linear and non-linear registration (http://cmrm.med.jhmi.edu/). Fractional anisotropy (FA) and mean diffusivity (MD) maps were estimated by fitting diffusion tensor model to each image voxel, and their mean values were computed within each ROI with PVE threshold. Participants of this study consisted of 20 healthy controls, 27 Alzheimer's disease and 27 normal-pressure hydrocephalus patients. The result showed that the mean FA and MD of each ROI were increased and decreased respectively, but standard deviation was significantly decreased when PVE was applied. In conclusion, the proposed method suggested that PVE was indispensable to improve an accuracy of DTI ROI study.

Keywords

References

  1. C. Pierpaoli, and P. J. Basser, "Toward a quantitative assessment of diffusion anisotropy", Magnetic resonance in medicine, vol. 36, no. 6, pp. 893-906, 1996. https://doi.org/10.1002/mrm.1910360612
  2. C. Beaulieu, "The basis of anisotropic water diffusion in the nervous system-a technical review", NMR in Biomedicine, vol. 15, no. 7-8, pp. 435-455, 2002. https://doi.org/10.1002/nbm.782
  3. P. N. Sen, and P. J. Basser, "A model for diffusion in white matter in the brain", Biophysical Journal, vol. 89, no. 5, pp. 2927-2938, 2005. https://doi.org/10.1529/biophysj.105.063016
  4. S. K. Song, S. W. Sun, W. K. Ju, S. J. Lin, A. H. Cross, and A. H. Neufeld, "Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia", Neuroimage, vol. 20, no. 3, pp. 1714-1722, 2003. https://doi.org/10.1016/j.neuroimage.2003.07.005
  5. J. H. Kim, M. D. Budde, H.-F. Liang, R. S. Klein, J. H. Russell, A. H. Cross, and S.-K. Song, "Detecting axon damage in spinal cord from a mouse model of multiple sclerosis", Neurobiology of disease, vol. 21, no. 3, pp. 626-632, 2006. https://doi.org/10.1016/j.nbd.2005.09.009
  6. Y. Hirata, H. Matsuda, K. Nemoto, T. Ohnishi, K. Hirao, F. Yamashita, T. Asada, S. Iwabuchi, and H. Samejima, "Voxelbased morphometry to discriminate early Alzheimer's disease from controls", Neuroscience letters, vol. 382, no. 3, pp. 269-274, 2005. https://doi.org/10.1016/j.neulet.2005.03.038
  7. B. B. Bendlin, M. L. Ries, E. Canu, A. Sodhi, M. Lazar, A. L. Alexander, C. M. Carlsson, M. A. Sager, S. Asthana, and S. C. Johnson, "White matter is altered with parental family history of Alzheimer's disease", Alzheimer's & Dementia, vol. 6, no. 5, pp. 394-403, 2010. https://doi.org/10.1016/j.jalz.2009.11.003
  8. S. M. Smith, M. Jenkinson, H. Johansen-Berg, D. Rueckert, T. E. Nichols, C. E. Mackay, K. E. Watkins, O. Ciccarelli, M. Z. Cader, and P. M. Matthews, "Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data", Neuroimage, vol. 31, no. 4, pp. 1487-1505, 2006. https://doi.org/10.1016/j.neuroimage.2006.02.024
  9. M. A. Petoe, W. D. Byblow, E. J. de Vries, V. Krishnamurthy, C. S. Zhong, P. A. Barber, and C. M. Stinear, "A templatebased procedure for determining white matter integrity in the internal capsule early after stroke", NeuroImage: Clinical, vol. 4, pp. 695-700, 2014. https://doi.org/10.1016/j.nicl.2013.12.006
  10. S. Mori, K. Oishi, H. Jiang, L. Jiang, X. Li, K. Akhter, K. Hua, A. V. Faria, A. Mahmood, and R. Woods, "Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template", Neuroimage, vol. 40, no. 2, pp. 570-582, 2008. https://doi.org/10.1016/j.neuroimage.2007.12.035
  11. D. J. Roybal, N. Barnea-Goraly, R. Kelley, L. Bararpour, M. E. Howe, A. L. Reiss, and K. D. Chang, "Widespread white matter tract aberrations in youth with familial risk for bipolar disorder", Psychiatry Research: Neuroimaging, vol. 232, no. 2, pp. 184-192, 2015. https://doi.org/10.1016/j.pscychresns.2015.02.007
  12. K. Oishi, A. Faria, H. Jiang, X. Li, K. Akhter, J. Zhang, J. T. Hsu, M. I. Miller, P. C. van Zijl, and M. Albert, "Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants", Neuroimage, vol. 46, no. 2, pp. 486-499, 2009. https://doi.org/10.1016/j.neuroimage.2009.01.002
  13. A. V. Faria, A. Hoon, E. Stashinko, X. Li, H. Jiang, A. Mashayekh, K. Akhter, J. Hsu, K. Oishi, and J. Zhang, "Quantitative analysis of brain pathology based on MRI and brain atlases-applications for cerebral palsy", Neuroimage, vol. 54, no. 3, pp. 1854-1861, 2011. https://doi.org/10.1016/j.neuroimage.2010.09.061
  14. J. Tohka, A. Zijdenbos, and A. Evans, "Fast and robust parameter estimation for statistical partial volume models in brain MRI", Neuroimage, vol. 23, no. 1, pp. 84-97, 2004. https://doi.org/10.1016/j.neuroimage.2004.05.007
  15. K. Kang, U. Yoon, W. Choi, and H. Lee, "Diffusion tensor imaging of idiopathic normal-pressure hydrocephalus and the cerebrospinal fluid tap test", Journal of the Neurological Sciences, vol. 364, pp. 90-96, 2016. https://doi.org/10.1016/j.jns.2016.02.067
  16. S. M. Smith, M. Jenkinson, M. W. Woolrich, C. F. Beckmann, T. E. Behrens, H. Johansen-Berg, P. R. Bannister, M. De Luca, I. Drobnjak, and D. E. Flitney, "Advances in functional and structural MR image analysis and implementation as FSL", Neuroimage, vol. 23, pp. S208-S219, 2004. https://doi.org/10.1016/j.neuroimage.2004.07.051
  17. R. P. Woods, S. T. Grafton, C. J. Holmes, S. R. Cherry, and J. C. Mazziotta, "Automated image registration: I. General methods and intrasubject, intramodality validation", Journal of computer assisted tomography, vol. 22, no. 1, pp. 139-152, 1998. https://doi.org/10.1097/00004728-199801000-00027
  18. R. P. Woods, S. T. Grafton, J. D. Watson, N. L. Sicotte, and J. C. Mazziotta, "Automated image registration: II. Intersubject validation of linear and nonlinear models", Journal of computer assisted tomography, vol. 22, no. 1, pp. 153-165, 1998. https://doi.org/10.1097/00004728-199801000-00028
  19. E. Luders, K. Clark, K. L. Narr, and A. W. Toga, "Enhanced brain connectivity in long-term meditation practitioners", Neuroimage, vol. 57, no. 4, pp. 1308-1316, 2011. https://doi.org/10.1016/j.neuroimage.2011.05.075
  20. J. G. Sled, A. P. Zijdenbos, and A. C. Evans, "A nonparametric method for automatic correction of intensity nonuniformity in MRI data", Medical Imaging, IEEE Transactions on, vol. 17, no. 1, pp. 87-97, 1998. https://doi.org/10.1109/42.668698
  21. S. M. Smith, "Fast robust automated brain extraction", Human brain mapping, vol. 17, no. 3, pp. 143-155, 2002. https://doi.org/10.1002/hbm.10062
  22. A. P. Zijdenbos, R. Forghani, and A. C. Evans, "Automatic" pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis", Medical Imaging, IEEE Transactions on, vol. 21, no. 10, pp. 1280-1291, 2002. https://doi.org/10.1109/TMI.2002.806283
  23. A. L. Alexander, J. E. Lee, M. Lazar, and A. S. Field, "Diffusion Tensor Imaging of the Brain", Neurotherapeutics, vol. 4, no. 3, pp. 316-329, 2007. https://doi.org/10.1016/j.nurt.2007.05.011