Regular ArticleFiber Tracking from DTI Using Linear State Space Models: Detectability of the Pyramidal Tract
References (22)
- et al.
Estimation of the effective self-diffusion tensor from the NMR spin echo
J. Magn. Reson. Series B
(1994) - et al.
Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI
J. Magn. Reson. Series B
(1996) - et al.
A comparative study of acquisition schemes for diffusion tensor imaging using MRI
J. Magn. Reson. Series B
(1999) - et al.
Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture
NeuroImage
(2001) - et al.
Regularization of diffusion-based direction maps for the tracking of brain white matter fascicles
NeuroImage
(2000) - et al.
Towards inference of human brain connectivity from MR diffusion tensor data
Med. Image Anal.
(2001) - et al.
Noise considerations in the determination of diffusion tensor anisotropy
J. Magn. Reson. Imag.
(2000) - et al.
Visualizing and characterizing white matter fiber structure and architecture in the human pyramidal tract using diffusion tensor MRI
Magn. Reson. Imag.
(1999) - et al.
Reconstruction of vector and tensor fields from sampled discrete data
Contemp. Math.
(1999) - et al.
Optimal Filtering
(1979)
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2022, World NeurosurgeryCitation Excerpt :Complementary IONM allows the resection to proceed carefully up to a functional boundary, the location of which can be confirmed with increased confidence, regardless of any brain shift or edema that often disrupts neuroimaging. Many have suggested the need to compare such methods with one another in terms of efficacy16,81; however, their synergistic benefits in providing surgeons with improved structural-functional information regardless of their differences also must be taken into consideration. Safe, maximal resection of brain tumors improves a patient’s onco-functional balance and is associated with improved outcomes.
Physical and digital phantoms for validating tractography and assessing artifacts
2021, NeuroImageCitation Excerpt :The most common way to create multi-voxel macroscopic fiber structures suitable for tractography analyses described in the literature is by using mathematical functions that model the course and shape of one or multiple fiber bundles in 2D or 3D. The earliest approaches of this type modelled relatively simple geometric bodies, such as lines, circles, ellipses, helices and fiber crossings or other shapes defined by simple mathematical functions (Gössl et al., 2002; Tournier et al., 2002; Lori et al., 2002, ; Lazar and Alexander, 2003; Kang et al., 2005; Kreher et al., 2005; Staempfli et al., 2006; Batchelor et al., 2006; Aganj et al., 2011; Cetingul et al., 2012; Wu et al., 2012) that define the local anisotropic diffusion, typically embedded in an isotropic non-fiber surrounding. Fig. 11 illustrates examples for this approach.
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2020, Neurophysiology in Neurosurgery: A Modern ApproachTracking and validation techniques for topographically organized tractography
2018, NeuroImageCitation Excerpt :Acknowledging the limitations of the noisy, low resolution dMRI data, there has been a shift towards addressing the uncertainty. This lead to several propagation or walker based solutions including techniques based on (i) front evolution and marching methods (Parker et al., 2002; Tournier et al., 2003; Kang et al., 2005; Pichon et al., 2005; Jackowski et al., 2005; Prados et al., 2006; Li et al., 2014), (ii) probabilistic and combinatorial techniques based on random walks and various sampling schemes (Bjrnemo et al., 2002; Behrens et al., 2003, 2007; Hagmann et al., 2003; Parker et al., 2003; Lu et al., 2006; Friman et al., 2006; Lifshits et al., 2009; Descoteaux et al., 2009; Tournier et al., 2012; Jeurissen et al., 2014), (iii) Kalman filtering (Gössl et al., 2002; Malcolm et al., 2009, 2010), (iv) bootstrap methods (Lazar and Alexander, 2005; Jones, 2008; Jeurissen et al., 2011; Vorburger et al., 2013; Campbell et al., 2014; Jeurissen et al., 2011, 2011), (v) graph theoretical techniques (Iturria-Medina et al., 2007; Sotiropoulos et al., 2010) and (vi) particle filtering (Zhang et al., 2009; Savadjiev et al., 2010; Pontabry et al., 2013; Stamm et al., 2013; Rowe et al., 2013). Simultaneous to these efforts, there have been several creative approaches proposed for a global solution using (i) fast marching methods and geodesics (Parker et al., 2002; O'Donnell et al., 2002; Campbell et al., 2005; Jbabdi et al., 2007a; Zalesky, 2008; Péchaud et al., 2009; Hageman et al., 2009; Lenglet et al., 2009) (ii) spin glass models (Mangin et al., 2002; Fillard et al., 2009) (iii) Bayesian model (Jbabdi et al., 2007b) (iv) Gibbs sampling (Kreher et al., 2008; Reisert et al., 2011) (v) Hough transform (Aganj et al., 2011) and (vi) ant colony optimization (Feng and Wang, 2011).
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