Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT)

By Evren Özarslan, Timothy M. Shepherd, Baba C. Vemuri, Stephen J. Blackband, Thomas H. Mareci. NeuroImage, 2006

444 citations recorded by the source corpus.

Subfields: Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d650b31324fa4cdd724d72b9741a1df7bcca1459

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In Pediatrics, Perinatology and Child Health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Regularized, fast, and robust analytical Q‐ball imaging (2007, background): “Frank (8), Alexander et al. (7), Zhan et al. (10), Tournier et al. (18), Ozarslan et al. (27), Anderson (23), Hess et al. (28,29), and others seem to have converged to this methodological tool which has many powerful properties.”
Advances in diffusion MRI acquisition and processing in the Human Connectome Project (2013, result): “Similar sensitivity differences between single and multi-shell approaches have been reported before (Aganj et al., 2010; Jbabdi et al., 2012; Ozarslan et al., 2006).”
Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data (2008, unknown): “To address this issue, a number of approaches have recently been proposed to estimate fibre orientations robustly in the presence of partial volume effects (Tuch, 2004; Wedeen et al., 2005; Jansons and Alexander, 2003; Özarslan et al., 2006; Tuch et al., 2002; Hosey et al., 2005; Behrens et al., 200”
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, methodology): “A similar approach has been proposed by Özarslan et al. (2006) who compute a Diffusion Orientation Transform by expressing the inverted Fourier integral by a Raleigh expansion assuming”
Pushing the limits of in vivo diffusion MRI for the Human Connectome Project (2013, methodology): “, 2004, 2008), diffusion orientation transform (Ozarslan et al., 2006) and persistent angular structure (Anderson, 2005).”
Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems (2012, methodology): “assumption in multi-shell data, the profiles will appear broader [42], reducing the angular resolution of the method.”
Diffusion Tensor Imaging and Beyond (2011, unknown): “Moreover, these methods cannot be used to infer the fiber orientations themselves without significant further processing (124).”
Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data (2015, background): “Anon-comprehensivelistincludesmodelsthatdirectlyextend conventional DTI (Tuch et al., 2002; Schultz and Seidel, 2008; Barmpoutis et al., 2009), Persistent Angular Structure (Jansons and Alexander, 2003), Diffusion Orientation Transform (Özarslan et al., 2006), Diffusion SpectrumImaging(Wedeenetal.,2”
Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions (2009, methodology): “Most of these methods produce representations of water molecule diffusion as a function of direction such as in the cases of Diffusion Spectrum Imaging (DSI) [6], Q-Ball Imaging (QBI) [7], Composite Hindered and Restricted Model of Diffusion (CHARMED) [8], Persistent Angular Structure (PAS) [9] Diff”
Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution (2011, methodology): “Similar methods were developed for HARDI-based reconstruction methods where the characteristics of the random walk are determined by the shape of the underlying orientation distribution functions (ODFs).”
Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution (2007, background): “The recently proposed diffusion orientation transform (Özarslan et al., 2006) assumes monoexponential signal decay, which has been shown to be not valid for high q-values in vivo (Cohen and Assaf, 2002).”