In the case of a single fiber, the method. Citation Context Q-ball imaging by David S. Tuch - Magnetic Resonance in Medicine , Magnetic resonance diffusion tensor imaging DTI provides a powerful tool for mapping neural histoarchitecture in vivo.
How-ever, DTI can only resolve a single fiber orientation within each imaging voxel due to the constraints of the tensor model. For example, DTI cannot resolve fibers crossing, be Abstract - Cited by 0 self - Add to MetaCart Magnetic resonance diffusion tensor imaging DTI provides a powerful tool for mapping neural histoarchitecture in vivo.
For example, DTI cannot resolve fibers crossing, bending, or twist-ing within an individual voxel. Intravoxel fiber crossing can be resolved using q-space diffusion imaging, but q-space imaging requires large pulsed field gradients and time-intensive sam-pling.
It is also possible to resolve intravoxel fiber crossing using mixture model decomposition of the high angular resolu-tion diffusion imaging HARDI signal, but mixture modeling requires a model of the underlying diffusion process.
Recently, it has been shown that the HARDI signal can be reconstructed model-independently using a spherical tomo-graphic inversion called the Funk—Radon transform, also known as the spherical Radon transform. The resulting imaging method, termed q-ball imaging, can resolve multiple intravoxel fiber orientations and does not require any assumptions on the diffusion process such as Gaussianity or multi-Gaussianity.
The present paper reviews the theory of q-ball imaging and de-scribes a simple linear matrix formulation for the q-ball recon-struction based on spherical radial basis function interpolation. Open aspects of the q-ball reconstruction algorithm are. Mareci - Magn. A new method for mapping diffusivity profiles in tissue is presented. The Bloch-Torrey equation is modified to include a diffusion term with an arbitrary rank Cartesian tensor.
This equation is solved to give the expression for the generalized Stejskal-Tanner formula quantifying diffusive attenuatio Abstract - Cited by 5 self - Add to MetaCart A new method for mapping diffusivity profiles in tissue is presented.
This equation is solved to give the expression for the generalized Stejskal-Tanner formula quantifying diffusive attenuation in complicated geometries. This makes it possible to calculate the components of higher-rank tensors without using the computationally-difficult spherical harmonic transform. General theoretical relations between the diffusion tensor DT components measured by traditional rank-2 DT imaging DTI and 3D distribution of diffusivities, as measured by high angular resolution diffusion imaging HARDI methods, are derived.
The relationships between higher- and lower-rank Cartesian DTs are also presented. The inadequacy of the traditional. Mapping human whole-brain structural networks with diffusion MRI.
Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and i Abstract - Cited by 47 2 self - Add to MetaCart Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience.
Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.
MR connectomics: principles and challenges. Methods , In the present work we review the current methods enabling structural connectivity mapping with MRI and show Abstract - Cited by 33 5 self - Add to MetaCart a b s t r a c t MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science.
In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. The Nuclear Many C.
High-resolution laser spectroscopy in fast beam, Optical Communications 17 Crossing of single-particle energy levels resulting from nneutron excess in the sd shell. Precision lifetime measurement for the 3p levels of MgII using frequency-doubled laser radiation to excite a fast ion beam.
Isotope shift calculations for atoms with one valence electron — Berengut, J. Changes in mean-square nuclear charge radii from optical isotope shifts, International Conference on Exotic Nuclei and Atomic Masses: Collapse of the conventional shell-model ordering in the very-neutron-rich isotopes of Na and Mg — Wildenthal, B.
Deformation of nuclei close to the two neutron dripline in Mg region — Terasaki, J. Changes in mean-square nuclear charge radii from optical isotope shifts — Heilig, K. Shell model study of the neutron rich isotopes from oxygen to silicon — Caurier, E. The first excited state of 30 Ne studied by proton resonancw scattering in reversed kinematics — Yanagisawa, Y et al.
Large-scale shell model calculations for exotic nuclei. Shape transition in the neutron rich sodium maggnetic — Campi, X. This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are as essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website.
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Ardelean, R. Kimmich, J. Oxford University Press Paul T. Schlumberger Figure 1. Author : Paul T.
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