HARDI & Tissue Characterization
Thursday 6 May 2010
Room A9 10:30-12:30 Moderators: Cristina Granziera and Geoffrey J. M. Parker

10:30 572. 

Reduced Encoding Persistent Angular Structure
Andrew Sweet1, Daniel C. Alexander1
1Department of Computer Science, University College London, London, United Kingdom

Persistent angular structure (PAS) MRI is a method that recovers complex white matter fibre configurations within single voxels of high angular resolution diffusion MRI (HARDI) data. It continues to exhibit impressive performance in comparison to other state of the art methods, but at the expense of a longer computation time. Here, we introduce a reduced encoding representation that cuts this computation time to around a quarter of its original value, while retaining performance on synthetic data. Minor differences between the reduced and original encoding are observed in real brain data, but do not necessarily represent decreased performance.

10:42 573. 

Estimating the Number of Fiber Orientations in Diffusion MRI Voxels: A Constrained Spherical Deconvolution Study
Ben Jeurissen1, Alexander Leemans2, Jacques-Donald Tournier3, Derek K. Jones4, Jan Sijbers1
1Visionlab, University of Antwerp, Antwerp, Belgium; 2Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands; 3Brain Research Institute, Florey Neuroscience Institutes (Austin), Melbourne, Victoria, Australia; 4CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom

Recent advances of high angular resolution diffusion imaging allow the extraction of multiple fiber orientations per voxel and have spawned an interest for classification of voxels by the number of fiber orientations. In this work, we estimated the number of fiber orientations within each voxel using the constrained spherical deconvolution method with the residual bootstrap approach. We showed that multiple-fiber profiles arise consistently in various regions of the human brain where complex tissue structure is known to exist. Moreover, we detect voxels with more than two fiber orientations and detect a much higher proportion of multi-fiber voxels than previously reported.

10:54 574. 

Can Spherical Deconvolution Give Us More Information Beyond Fibre Orientation? Towards Novel Quantifications of White Matter Integrity
Flavio Dell'Acqua1, Andrew Simmons1, Steven Williams1, Marco Catani1
1Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, United Kingdom

In recent years Spherical Deconvolution methods have been applied to diffusion imaging to improve the visualization of multi-fibre orientation in brain regions with complex white matter organization. However, the potential to quantify white matter integrity with SD has not been explored. In this study we show that assuming a fibre response function based on a restricted diffusion model may lead to a better interpretation of spherical deconvolution results, relaxing the requirement of an exact knowledge of the fibre response and possibly help the development of new fibre specific indices of white matter integrity.

11:06 575.  

Apparent Fibre Density: A New Measure for High Angular Resolution Diffusion-Weighted Image Analysis - not available
David Raffelt1,2, Stuart Crozier2, Alan Connelly3,4, Olivier Salvado1, J-Donald Tournier3,4
1The Australian E-Health Research Centre, CSIRO, Brisbane, QLD, Australia; 2Department of Biomedical Engineering, University of Queensland, Brisbane, QLD, Australia; 3Brain Research Institute, Florey Neuroscience Institutes (Austin), Melbourne, VIC, Australia; 4Department of Medicine, University of Melbourne, Melbourne, VIC, Australia

Apparent Fibre Density is a new measure that is based on information provided by Fibre Orientation Distributions. Voxel wise comparisons of Apparent Fibre Density can be made over all orientations permitting differences to be attributed to a single fibre within voxels with multiple fibre populations.

11:18 576.

Dependence of Axon Diameter Index on Maximum Gradient Strength
Tim B. Dyrby1, Penny L. Hubbard2, Maurice Ptito3, Matt G. Hall4, Daniel C. Alexander4
1Danish Research Centre for Magnetic Resonance, Copenhagen Univerviersity Hospital, Hvidovre, Denmark; 2Imaging Science and Biomedical Imaging, University of Manchester, Manchester, United Kingdom; 3School of Optometry, University of Montreal, Montreal, Canada; 4Centre for Medical Image Computing, University College London, London, United Kingdom

We aimed to elucidate the dependence of the axon diameter index on the maximum available gradient strength (Gmax). Optimised protocols were produced that were sensitive to a-priori axon diameters of 1, 2 and 4 μ m for Gmax = 60, 140, 200 and 300mT/m, and data were acquired on fixed monkey brain.  The mapped axon diameter index was sensitive to Gmax but relatively constant for >140mT/m. Simulations suggest that at low Gmax (60mT/m), axon diameters <3μ m are indistinguishable, which explains the unexpectedly high values at low Gmax.

11:30 577. 

The Analytic Distribution of Fractional Anisotropy in Diffusion MRI
Leigh A. Johnston1,2, Adel Foda2, Michael J. Farrell2,3, Gary F. Egan2,3
1School of Engineering & NICTA VRL, University of Melbourne, Melbourne, VIC, Australia; 2Howard Florey Institute, Florey Neuroscience Institutes, Melbourne, VIC, Australia; 3Centre for Neuroscience, University of Melbourne, Melbourne, VIC, Australia

Statistical analyses of fractional anisotropy images in diffusion MRI studies are traditionally approached using parametric tests, under Gaussianity assumptions, or nonparametric resampling techniques.  We present an analytic form for the distribution of FA, both for Gaussian distributed tensor eigenvalues for which FA follows a transformed doubly noncentral beta distribution, and a generalisation to arbitrary eigenvalue distributions.  These powerful result permits application of valid inference statistical tests to FA maps in all experimental conditions.

11:42 578. 

Probabilistic Quantification of Regional Cortical Microstructural Complexity
Hamied Ahmad Haroon1,2, Richard J. Binney2,3, Geoff J. M. Parker

1Imaging Science and Biomedical Engineering, School of Cancer and Imaging Sciences, The University of Manchester, Manchester, England, United Kingdom; 2The University of Manchester Biomedical Imaging Institute, The University of Manchester, Manchester, England, United Kingdom; 3Neuroscience and Aphasia Research Unit, School of Psychological Sciences, The University of Manchester, Manchester, England, United Kingdom

Model-based residual bootstrapping applied to constrained spherical deconvolution analysis of HARDI provides probabilities of observing n fiber orientations in every voxel of the brain. We hypothesized that the distribution of these probabilities for each n within cortical and subcortical regions would reflect the varying underlying neural microstructural complexity associated with each. We show evidence supporting this hypothesis and show consistency between hemispheres and amongst a small group of healthy subjects. This may offer non-invasive sensitivity to cortical cytoarchitecture that may be useful in cortical parcellation and in the identification of cortical lesions.

11:54 579. 

The FA Connectome: A Quantitative Strategy for Studying Neurological Disease Processes
Stephen Rose1,2, Kerstin Pannek1,3, Olivier Salvado4, Parnesh Raniga4, Fusun Baumann5, Robert Henderson5
1UQ Centre for Clinical Research, University of Queensland, Brisbane, Queensland, Australia; 2Centre for Medical Diagnostic Technologies in Queensland, University of Queensland, Brisbane, Queensland, Australia; 3Centre for Magnetic Resonance, University of Queensland, Brisbane, Queensland, Australia; 4The Australian e-Health Research Centre, CSIRO, Brisbane, Queensland, Australia; 5Neurology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia

Structural connectivity indices derived using diffusion based HARDI or q-ball imaging in conjunction with functional parcellation of the cortex from high resolution MRI, has provided insight into the anatomical conformation of many of the important neural networks in the living brain. We are developing the concept of the FA connectome, i.e. combining a measure of fractional anisotropy, a quantitative diffusivity metric that reflects the integrity of WM pathways, with the connectivity matrix. When applied to study Amyotrophic Lateral Sclerosis, this technique shows identifies a number of key corticomotor pathways with reduced mean FA compared to control participants.

12:06 580.

Novel Spherical Phantoms for Q-Ball Imaging Under in Vivo Conditions
Amir Moussavi1, Bram Stieltjes2, Klaus H. Fritzsche3, Frederik B. Laun4
1Medical Physics in Radiology, German  Cancer Research Center, Heidelberg, Germany; 2Radiology, German Cancer Research  Center, Heidelberg, Germany; 3Medical Imaging and Biological Informatics, German Cancer Research Center, Heidelberg, Germany; 4Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany

Spherical shaped diffusion phantoms that mimic in vivo fiber crossings are presented. Two crossing angles (45░ and 90░) and two packing types of the fibers in the crossing were realized (stacked and interleaved). The fractional anisotropy of individual fibers is can be adjusted between 0.52 and 0.95. High quality ODF maps with a voxel resolution of 2x2x5 mm│ were acquired using a standard diffusion weighted echoplanar diffusion sequence. Thus, the presented phantoms allow for validity measurements of Q-ball imaging and reconstruction approaches.

12:18 581

A Diffusion Hardware Phantom Looking Like a Coronal Brain Slice
Cyril Poupon1, Laurent Laribiere1, Gregory Tournier1, Jeremy Bernard1, Denis Fournier1, Pierre Fillard1, Maxime Descoteaux2, Jean-Francois Mangin1
CEA I2BM NeuroSpin, Gif-sur-Yvette, France; 2UniversitÚ de Sherbrooke, Sherbrooke, Quebec, Canada

Diffusion-weighted imaging has become an established technique to infer the micro-structure of the brain. Its more popular application, fiber tractography, is still the only possibility to infer in vivo the structural connectivity of the brain. Despite the plethora of tractography algorithms in the literature, it is almost impossible to validate them. In this work, we present a novel hardware phantom dedicated to the validation of HARDI models and tractography algorithms. Its geometry was designed to mimic a coronal slice location of a human brain, depicting a large set of specific configurations (crossings, kissings, splittings)



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