مقایسهٔ روش‌ها

	ایمیجینگ کورتوز انتشار	تصویربرداری پراکندگی و چگالی جهت‌گیری نوریت (NODDI)	مورفومتری مبتنی بر وکسل
حوزه	تصویربرداری عصبی	تصویربرداری عصبی	تصویربرداری عصبی
خانواده	Process / pipeline	Process / pipeline	Process / pipeline
سال پیدایش	2005	2012	2000
پدیدآور	Jens Jensen	Hui Zhang	John Ashburner
نوع	Microstructural white matter analysis	Microstructural white matter mapping	Structural MRI gray matter analysis pipeline
منبع بنیادین	Jensen, J. H., Helpern, J. A., Ramani, A., et al. (2005). Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by magnetic resonance imaging. Magnetic Resonance in Medicine, 53(6), 1432–1440.	Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage, 61(4), 1000–1016.	Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—the methods. NeuroImage, 11(6), 805–821.
نام‌های دیگر	DKI, non-Gaussian diffusion, diffusion kurtosis	NODDI, neurite density mapping	VBM, grey matter morphometry
مرتبط	3	3	2
خلاصه	Diffusion Kurtosis Imaging (DKI) is an advanced diffusion MRI technique that quantifies non-Gaussian diffusion of water molecules, providing detailed information about tissue microstructure beyond conventional diffusion tensor imaging. Introduced by Jensen and colleagues in 2005, DKI detects deviations from normal Gaussian diffusion, revealing information about cellular compartmentalization and fiber heterogeneity.	Neurite Orientation Dispersion and Density Imaging (NODDI) is a biophysical diffusion MRI model that quantifies microstructural properties of white matter: neurite density (axonal density), orientation dispersion (fiber coherence), and isotropic diffusion (free water or cerebrospinal fluid). Introduced by Zhang and colleagues in 2012, NODDI provides biologically interpretable metrics directly linking diffusion MRI signals to tissue microstructure.	Voxel-Based Morphometry (VBM) is a whole-brain statistical technique for detecting local differences in gray matter volume or concentration from structural MRI. Introduced by John Ashburner and Karl Friston in 2000, VBM enables researchers to identify regional brain volume changes associated with disease, aging, learning, and other factors without requiring a priori region-of-interest definitions.