Regularized, fast, and robust analytical Q‐ball imaging

By Maxime Descoteaux, Elaine Angelino, Shaun Fitzgibbons, Rachid Deriche. Magnetic Resonance in Medicine, 2007

944 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/53568837a5707a23e5134b32649e409cf93cad72

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data (2015, methodology): “Empirical procedures are commonly employed in the ﬁ eld for setting the regularization trade-off of similar ill-posed problems; see for instance classic methods like Tournier et al. (2007); Descoteaux et al. (2007); and Landman et al. (2012).”
Pushing the limits of in vivo diffusion MRI for the Human Connectome Project (2013, methodology): “Here the diffusion ODF was reconstructed using the spherical harmonic decomposition (Anderson, 2005; Descoteaux et al., 2007; Hess et al., 2006) with sharpening (Kezele et al.”
Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy (2013, methodology): “GFA was calculated using the ODF from q-ball imaging [33].”
Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions (2009, methodology): “lytical ODF estimation presented in Section III-A, [23].”
Tractography - where do we go from here? (2011, positive): “Alternatives have also been suggested (Aganj et al., 2010; Descoteaux et al., 2007; Hess et al., 2006; Wedeen et al., 2008) and successfully applied to studying the architecture of white matter in humans (Hag-mann et al., 2007) and non-human animals (Schmahmann et al., 2007).”
Dipy, a library for the analysis of diffusion MRI data (2014, methodology): “This q-ball ODF ψ QBI can be obtained analytically from a SH estimation of the diffusion signal (Anderson, 2005; Hess et al., 2006; Descoteaux et al., 2007): where P l ( j ) is the Legendre polynomial of order l corresponding to coefﬁcient j and the coefﬁcients have been normalized by the S0…”
Advances in diffusion MRI acquisition and processing in the Human Connectome Project (2013, methodology): “Amongst them, various methods for resolving diffusion ODFs (Aganj et al., 2010; Descoteaux et al., 2007; Kamath et al., 2012; Tuch, 2004), as well as methods for non-parametric spherical deconvolution (Tournier et al.”
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).”