부분용적효과를 고려한 확산텐서영상에 대한 관심영역 분석 연구
ROI Study for Diffusion Tensor Image with Partial Volume Effect
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초록
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.
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.
참고문헌 (23)
- C. Pierpaoli, "Toward a quantitative assessment of diffusion anisotropy", Magnetic resonance in medicine, 36(6), pp.893~906 (1996)
- C. Beaulieu, "The basis of anisotropic water diffusion in the nervous system-a technical review", NMR in Biomedicine, 15(7-8), pp.435~455 (2002)
- P. N. Sen, "A model for diffusion in white matter in the brain", Biophysical Journal, 89(5), pp.2927~2938 (2005)
- S. K. Song, "Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia", Neuroimage, 20(3), pp.1714~1722 (2003)
- J. H. Kim, "Detecting axon damage in spinal cord from a mouse model of multiple sclerosis", Neurobiology of disease, 21(3), pp.626~632 (2006)
- Y. Hirata, "Voxelbased morphometry to discriminate early Alzheimer’s disease from controls", Neuroscience letters, 382(3), pp.269~274 (2005)
- B. B. Bendlin, "White matter is altered with parental family history of Alzheimer’s disease", Alzheimer’s & Dementia, 6(5), pp.394~403 (2010)
- S. M. Smith, "Tract-based spatial statistics : voxelwise analysis of multi-subject diffusion data", Neuroimage, 31(4), pp.1487~1505 (2006)
- M. A. Petoe, "A templatebased procedure for determining white matter integrity in the internal capsule early after stroke", NeuroImage : Clinical, 4, pp.695~700 (2014)
- S. Mori, "Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template", Neuroimage, 40(2), pp.570~582 (2008)
- D. J. Roybal, "Widespread white matter tract aberrations in youth with familial risk for bipolar disorder", Psychiatry Research : Neuroimaging, 232(2), pp.184~192 (2015)
- K. Oishi, "Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping : application to normal elderly and Alzheimer’s disease participants", Neuroimage, 46(2), pp.486~499 (2009)
- A. V. Faria, "Quantitative analysis of brain pathology based on MRI and brain atlases-applications for cerebral palsy", Neuroimage, 54(3), pp.1854~1861 (2011)
- J. Tohka, "Fast and robust parameter estimation for statistical partial volume models in brain MRI", Neuroimage, 23(1), pp.84~97 (2004)
- K. Kang, "Diffusion tensor imaging of idiopathic normal-pressure hydrocephalus and the cerebrospinal fluid tap test", Journal of the Neurological Sciences, 364, pp.90~96 (2016)
- S. M. Smith, "Advances in functional and structural MR image analysis and implementation as FSL", Neuroimage, 23, pp.S208~S219 (2004)
- R. P. Woods, "Automated image registration : I. General methods and intrasubject, intramodality validation", Journal of computer assisted tomography, 22(1), pp.139~152 (1998)
- R. P. Woods, "Automated image registration : II. Intersubject validation of linear and nonlinear models", Journal of computer assisted tomography, 22(1), pp.153~165 (1998)
- E. Luders, "Enhanced brain connectivity in long-term meditation practitioners", Neuroimage, 57(4), pp.1308~1316 (2011)
- J. G. Sled, "A nonparametric method for automatic correction of intensity nonuniformity in MRI data", Medical Imaging, IEEE Transactions on, 17(1), pp.87~97 (1998)
- S. M. Smith, "Fast robust automated brain extraction", Human brain mapping, 17(3), pp.143~155 (2002)
- A. P. Zijdenbos, "Automatic"pipeline"analysis of 3-D MRI data for clinical trials : application to multiple sclerosis", Medical Imaging, IEEE Transactions on, 21(10), pp.1280~1291 (2002)
- A. L. Alexander, "Diffusion Tensor Imaging of the Brain", Neurotherapeutics, 4(3), pp.316~329 (2007)
