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ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data
Introduction Diffusion tensor imaging (DTI) is becoming a standard addition to routine MR imaging for investigating microstructural tissue properties (e.g., see [1] for a recent review). With this…
Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data
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Denoising of diffusion MRI using random matrix theory
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Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging
TLDR
More robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor‐derived analyses are provided, using large, high‐quality diffusion‐weighted data sets, with reconstruction parameters optimized specifically for this task.
View on Wiley
DART: A Practical Reconstruction Algorithm for Discrete Tomography
TLDR
An iterative reconstruction algorithm for discrete tomography, called discrete algebraic reconstruction technique (DART), which is capable of computing more accurate reconstructions from a small number of projection images, or from asmall angular range, than alternative methods.
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Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls
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Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution
TLDR
This work introduced probabilistic tractography, which considers multiple possible pathways emanating from one seed point, taking into account the uncertainty of local fiber orientations, and the residual bootstrap, which is used to estimate fiber tract probability within a clinical time frame.
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Extraction of Airways From CT (EXACT'09)
TLDR
A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.
View on PubMed
Maximum likelihood estimation of signal amplitude and noise variance from MR data
TLDR
This article addresses the question whether it is better to use complex valued data or magnitude data for the estimation of these parameters using the maximum likelihood method and uses the mean‐squared error (MSE) as a performance criterion.
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Maximum-likelihood estimation of Rician distribution parameters
TLDR
In contrast to previously proposed methods, ML estimation is demonstrated to be unbiased for high signal-to-noise ratio (SNR) and to yield physical relevant results for low SNR.
View on IEEE
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