Creation of 3D Volume Loss Mask and Mean Performance Analysis on 3d MR Images
Keywords:, Voxel based morphometry, Alzheimer, Family wise error, Volume loss mask, Masking
Examination of changes in the brain during disease progression using numerical methods is one of the current topics investigated by neuroscience. One of the numerical methods developed for locally or globally analyzing changes in the brain is voxel based morphometry. With voxel based morphometry, volume differences between the intra and intergroup Magnetic Resonance images can be examined. The general voxel based morphometry studies performed in the literature are only mapped. However, tissue analysis and artificial intelligence studies using voxel based morphometry are inadequate. In this study, voxel based morphometry was used to create a three dimensional volume loss mask for Alzheimer's disease to use tissue analysis and artificial intelligence in the future studies. Two different masks were created for according to the usage of family wise error. Volume loss regions are cut from normalized segmented and smoothed normalized segmented gray matter images with the generated masks. The average values of the voxel values in these regions are statistically compared for four different scenarios. As a result of the comparison, segmentation made with normalized segmented and family wise error values have been more meaningful results than the other three scenarios.
(1) Ascoli, G.A., Computational neuroanatomy: Principles and methods2002: Springer Science & Business Media.
(2) Weiskopf, N., et al., Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology. Current opinion in neurology, 2015. 28(4): p. 313-322.
(3) Ashburner, J., et al., Computer-assisted imaging to assess brain structure in healthy and diseased brains. The Lancet Neurology, 2003. 2(2): p. 79-88.
(4) Mechelli, A., et al., Voxel-based morphometry of the human brain: methods and applications. Current medical Imaging reviews, 2005. 1(2): p. 105-113.
(5) Baron, J., et al., In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. Neuroimage, 2001. 14(2): p. 298-309.
(6) Chetelat, G., et al., Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport, 2002. 13(15): p. 1939-1943.
(7) Chetelat, G., et al., Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage, 2005. 27(4): p. 934-946.
(8) Marcus, D.S., et al., Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience, 2007. 19(9): p. 1498-1507.
(9) Glickman, M.E., S.R. Rao, and M.R. Schultz, False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. Journal of clinical epidemiology, 2014. 67(8): p. 850-857
(10) Nichols, T. and S. Hayasaka, Controlling the familywise error rate in functional neuroimaging: a comparative review. Statistical methods in medical research, 2003. 12(5): p. 419-446.
(11) Dashjamts, T., et al., Alzheimer's disease: diagnosis by different methods of voxel-based morphometry. Fukuoka Igaku Zasshi, 2012. 103(3): p. 59-69.
(12) Radua, J., et al., Validity of modulation and optimal settings for advanced voxel-based morphometry. Neuroimage, 2014. 86: p. 81-90.
(13) Aggarwal, N., B. Rana, and R. Agrawal, 3d discrete wavelet transform for computer aided diagnosis of Alzheimer's disease using t1‐weighted brain MRI. International Journal of Imaging Systems and Technology, 2015. 25(2): p. 179-190.
(14) Pfefferbaum, A., et al., A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Archives of neurology, 1994. 51(9): p. 874-887.
(15) Hu, H.-H., et al., Shape and curvedness analysis of brain morphology using human fetal magnetic resonance images in utero. Brain Structure and Function, 2013. 218(6): p. 1451-1462.
(16) Keller, S.S. and N. Roberts, Measurement of brain volume using MRI: software, techniques, choices and prerequisites. J Anthropol Sci, 2009. 87: p. 127-151.
(17) Penny, W.D., et al., Statistical parametric mapping: the analysis of functional brain images2011: Academic press.
(18) Jenkinson, M., et al., Fsl. Neuroimage, 2012. 62(2): p. 782-790.
(19) Fischl, B., FreeSurfer. Neuroimage, 2012. 62(2): p. 774-781.
(20) Ashburner, J., et al., SPM8 manual. Functional Imaging Laboratory, Institute of Neurology, 2008: p. 41.
(21) Kurth, F., E. Luders, and C. Gaser, VBM8 toolbox manual. Jena: University of Jena, 2010.
(22) Manjón, J.V., et al., Adaptive non‐local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging,
31(1): p. 192-203.
(23) Myronenko, A. and X. Song, Intensity-based image registration by minimizing residual complexity. IEEE Transactions on Medical Imaging, 2010. 29(11): p. 1882-1891.
(24) Vemuri, P. and C.R. Jack, Role of structural MRI in Alzheimer's disease. Alzheimer's research & therapy, 2010. 2(4): p. 23.
(25) Savio, A., et al., Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI. Computers in
biology and medicine, 2011. 41(8): p. 600-610.
(26) Ashburner, J., A fast diffeomorphic image registration algorithm. Neuroimage, 2007. 38(1): p. 95-113.
(27) Goto, M., et al., Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra provides reduced effect of scanner for cortex volumetry with atlas-based method in healthy subjects. Neuroradiology, 2013. 55(7): p. 869-875.
(28) Klein, A., et al., Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage, 2009. 46(3): p. 786-802.