NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain

By Hui Zhang, Torben Schneider, Claudia A. M. Gandini Wheeler-Kingshott, Daniel C. Alexander. NeuroImage, 2012

3,183 citations recorded by the source corpus.

Subfields: Orthopedics and Sports Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/305144fbb66544930f7ecc850b8743f31537ad2f

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In Orthopedics and Sports Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (2014, neutral): “, 2005), and neurite orientation dispersion and density imaging (Zhang et al., 2012).”
Multi-compartment microscopic diffusion imaging (2016, methodology): “…uthor M anuscript A uthor M anuscript model overcomes key limitations in existing techniques like WMTI (Fieremans et al., 2011) or NODDI (Zhang et al., 2012) as we make no assumptions about the neurite orientation distribution (e.g. single orientations, spherical harmonics or mixtures…”
Imaging brain microstructure with diffusion MRI: practicality and applications (2019, methodology): “However, in general, the effects of orientation dispersion dominate such contrast and more sophisticated models are necessary to separate the effects.(5) This section reviews such models in the context of assessing brain tissue microstructure.”
SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI (2019, positive): “Following (Zhang et al., 2012; Novikov et al., 2018b), we brie ﬂ y recall here that in general the normalised DW-MRI signal associated with the intra-neurite compartment can be represented as a convolution be-tween the ﬁ bre orientation distribution function (fODF) Р ð b n Þ and the response kernel…”
The Development of Brain White Matter Microstructure (2018, methodology): “These include the composite hindered and restricted CHARMED model (Assaf and Basser, 2005) and neurite orientation dispersion and density imaging (NODDI) (Zhang et al., 2012), which provide additional architectural information, including axonal and neurite density (NDI) and orientation dispersion in”
Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data (2015, methodology): “We tested AMICO using data and experimental settings used in the original publications, respectively (Alexander et al., 2010) for ActiveAx and (Zhang et al., 2012) for NODDI.”
Advances in diffusion MRI acquisition and processing in the Human Connectome Project (2013, background): “Estimating F is a deconvolution operation (Alexander, 2005; Anderson, 2005; Behrens et al., 2003; Dell'Acqua et al., 2007; Kaden et al., 2007; Sotiropoulos et al., 2012; Tournier et al., 2004; Zhang et al., 2012).”
Effects of aging on functional and structural brain connectivity☆ (2017, background): “Therefore, recent advances in diffusion imaging acquisition and analysis aim to improve estimates of tissue microstructure and fiber reconstruction (Raffelt et al., 2012; Wu et al., 2016; Zhang et al., 2012).”
Sex differences in the adult human brain: Evidence from 5,216 UK Biobank participants (2017, background): “, extracellular water), and restricted anisotropic diffusion (e.g., intra-axonal compartments; Zhang et al. 2012).”
q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (2015, methodology): “Advanced diﬀusion MRI models such as DKI [1] and NODDI [2] are preferable over traditional diﬀusion MRI models because they provide more accurate characterization of tissue microstructure.”
Investigating white matter fibre density and morphology using fixel-based analysis (2017, methodology): “In the context of voxel-averaged DWI measures (e.g. Alexander et al., 2010; Zhang et al., 2012), the ‘density’ of a voxel will also be influenced by partial volume with cerebral spinal fluid.”