| HARDI & Higher Order Descriptions of Diffusion |
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Tuesday 21 April 2009 |
| Room 316BC |
16:00-18:00 |
Moderators: |
Daniel Alexander and Mariana Lazar |
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16:00 |
357. |
Orientationally Invariant
Axon-Size and Density Weighted MRI |
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Daniel C. Alexander1,
Penny L. Hubbard2, Matt G. Hall1,
Elizabeth A. Moore3, Maurice Ptito4,
Geoff J. M. Parker2, Tim B. Dyrby5
1Centre for Medical Image Computing, Dept.
Computer Science, UCL (University College London),
London, UK; 2Imaging Science and
Biomedical Engineering, School of Cancer and Imaging
Sciences, University of Manchester, Manchester, UK;
3Best, Philips Healthcare, Eindhoven,
Netherlands; 4School of Optometry,
University of Montreal, Montreal, Canada; 5Danish
Research Centre for Magnetic Resonance, Copenhagen
University Hospital, Copenhagen, Denmark |
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We present a diffusion
MRI technique for mapping correlates of the axon
size and density in white matter over the whole
brain. Unlike previous techniques, the method does
not require prior knowledge of the fibre direction.
Results from a perfusion fixated monkey brain agree
with histological data in the literature. Further
results from in vivo human brain data,
acquired in less than one hour on a standard system,
also show the expected trends. |
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16:12 |
358. |
How Many Diffusion
Gradient Directions Are Required for HARDI? |
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J-Donald Tournier1,2, Fernando
Calamante1,2, Alan Connelly1,2
1Brain Research Institute, Florey Neuroscience
Institutes (Austin), Melbourne, Victoria, Australia;
2Department of Medicine, University of
Melbourne, Melbourne, Victoria, Australia |
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While the number of DW
directions required for DTI has been extensively
studied, the equivalent studies have not been
performed for HARDI. However, due to the large
number of directions inherent in HARDI acquisitions,
this information is crucial to keep scan times
practical. In this study, we use sampling theory to
determine the minimum number of DW directions
required for HARDI experiments, independent of the
particular algorithm used to estimate fibre
orientations. Results obtained at b = 3000s/mm2
indicate that 45 directions are required to
adequately characterize the angular frequency
information contained in the DW signal. |
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16:24 |
359. |
Diffusion Kurtosis Imaging (DKI) Reveals an Early
Phenotype (P30) in a Transgenic Rat Model for
Huntington’s Disease |
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Ines Blockx1,
Marleen Verhoye1,2, Geert De Groof1,
Johan Van Audekerke1, Kerstin Raber3,
Dirk Poot2, Jan Sijbers2,
Stephan von Horsten3, Annemie Van der
Linden1
1Bio-Imaging Lab, University of Antwerp,
Antwerp, Belgium; 2Vision Lab, University
of Antwerp, Antwerp, Belgium; 3Experimental
Therapy, Friedrich-Alexander University, Erlangen,
Germany |
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The transgenic rat model
for Huntington’s Disease (HD) resembles the
late-onset form, exhibiting a behavioral phenotype
with emotional disturbance, motor deficits, and
cognitive decline. We used Diffusion Kurtosis
Imaging (DKI) to study very young (P15-P30) HD rats
and controls. In white matter of P30 HD pups we
observed a significantly decreased fractional
anisotropy and increased axial kurtosis as compared
to controls. In grey matter of P30 HD pups axial
kurtosis was significantly increased as well as Mean
Kurtosis. The DKI changes we detected in HD pups,
suggests that neurodegenerative processes of HD also
involves neurodevelopment defects already detectable
at P30. |
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16:36 |
360. |
In Vivo Imaging of
Kurtosis Tensor Eigenvalues in the Brain at 3 T |
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Eric Edward Sigmund1,
Mariana Lazar1, Jens H. Jensen1,
Joseph A. Helpern1
1Radiology, New York University, New York, NY
, USA |
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Diffusion kurtosis
imaging (DKI) quantifies the well-known non-gaussianity
of apparent diffusion in biological tissue, and its
directionally averaged mean kurtosis (MK) has
potential clinical utility in the brain. However,
the complete rank-4 kurtosis tensor contains more
information that may be reduced, as in diffusion
tensor diagonalization, to characteristic
properties. The present study applies the recently
described spectral decomposition to derive the
eigenvalues/eigentensors of the kurtosis tensor, and
resolve them spatially in a full brain 3 T DW-MRI
scan. Maps of kurtosis eigenvalues and composite
“eigensurfaces” derived from eigentensor projections
present new contrast, potentially involving fiber
crossing and barrier-density anisotropy. |
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16:48 |
361. |
Detection of Brain
Maturation - DTI with Different B-Values Versus
Diffusion Kurtosis Imaging |
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Matthew Man Hin
Cheung1,2, Ed X. Wu1,2
1Department of Electrical and Electronic
Engineering, The University of Hong Kong, Hong Kong
SAR, China; 2Laboratory of Biomedical
Imaging and Signal Processing , The University of
Hong Kong, Hong Kong SAR, China |
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This study aims to
investigate the effects of different b-values in
detecting microstructural changes during
well-controlled rodent brain maturation. The results
are also compared to the diffusion kurtosis indices
obtained by diffusion kurtosis imaging (DKI) that
characterizes the restricted diffusion by fitting
multiple b-value DW measurements to a
quadratic-exponential model. The results indicate
that the b-value for optimal DTI detection of
microstructural changes depends on the specific
physiological or pathological processes targeted.
High-order diffusion imaging, such as DKI, is
therefore essential for more robust MR diffusion
characterization of neural tissues. |
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17:00 |
362. |
Super-Resolving Patches in
Diffusion MRI Using Canonical Fibre Configurations |
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Simon Jeremy Damion
Prince1, Daniel C. Alexander1
1Computer Science, University College London,
London, UK |
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We aim to improve the
spatial resolution of fibre-orientation estimates in
diffusion MRI by learning a prior over 3x3x3 voxel
patches that assumes self-similarity across scale.
In training we align and cluster patches of fibre
orientations from a larger scale using a mixture of
Watsons model. The resulting canonical fibre
configurations describe homogenous regions, bending,
fanning etc. To super-resolve, we find the canonical
patch and mean orientation, diffusivity and volume
fraction that best describe the voxel measurements.
We compare our results to nearest-neighbour
interpolation and demonstrate that it is possible to
successfully determine sub-voxel structure. |
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17:12 |
363. |
Model-Based Residual Bootstrap
of Constrained Spherical Deconvolution for
Probabilistic Segmentation and Tractography |
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Hamied Ahmad Haroon1,
David M. Morris1, Karl V. Embleton1,2,
Geoff J. Parker1
1Imaging Science and Biomedical Engineering,
School of Cancer and Imaging Sciences, The
University of Manchester, Manchester, England, UK;
2School of Psychological Sciences, The
University of Manchester, Manchester, England, UK |
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Here we describe the
application of model-based residual bootstrapping to
the analysis of HARDI data using constrained
spherical deconvolution. We demonstrate that the
method is able to provide estimates of the
probability of finding different fiber
configurations within the brain. These distributions
of fiber orientations may then be used directly as
PDFs across each configuration for probabilistic
tractography. This method provides a means by which
the microstructural complexity of tissue, as
reflected in the HARDI diffusion signal, may be
characterised, naturally accounting for the
underlying tissue microscopic complexity,
macroscopic partial volume, and data noise levels. |
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17:24 |
364. |
Diffusion Propagator Imaging:
A Novel Technique for Reconstructing the Diffusion
Propagator from Multiple Shell Acquisitions |
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Maxime Descoteaux1,
Jean-François Mangin1, Cyril Poupon1
1NeuroSpin, IFR 49, CEA Saclay, Gif-sur-Yvette,
France |
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We present a simple and
linear analytical diffusion propagator solution
using spherical Laplace's equation. The
reconstruction is possible from only two b-value
HARDI acquisitions and less than 100 diffusion
measurements. |
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17:36 |
365. |
Practical Crossing Fiber
Imaging with Combined DTI Datasets and Generalized
Reconstruction Algorithm |
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Fang-Cheng Yeh1,
Van Jay Wedeen2, Wen-Yih Isaac Tseng1,3
1Center for Optoelectronic Biomedicine,
National Taiwan University College of Medicine,
Taipei, Taiwan; 2MGH Martinos Center for
Biomedical Imaging, Harvard Medical School,
Charlestown, MA, USA; 3Department of
Medical Imaging, National Taiwan University
Hospital, Taipei, Taiwan |
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We present a clinically
feasible sampling scheme which combines two DTI
datasets to achieve higher angular resolution. The
performance and accuracy of the proposed scheme was
examined in comparison with q-ball imaging method. |
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17:48 |
366. |
Restriction Spectrum Imaging (RSI):
A New Method for Resolving Complex Tissue
Microstructures in Diffusion MRI |
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Nathan S. White1,
Trygve B. Leergaard2, Alex de Crespigny3,4,
Anders M. Dale5
1Cognitive Science, University of California,
San Diego, La Jolla, CA, USA; 2Centre for
Molecular Biology and Neuroscience, University of
Oslo, Norway; 3Radiology and
Neurosciences, Massachusetts General Hospital,
Harvard University; 4Department of
Clinical Neurology, Oxford University, UK; 5Neurosciences
and Radiology, University of California, San Diego,
La Jolla, CA, USA |
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We present a new
model-based analysis method for diffusion MRI called
Restriction Spectrum Imaging (RSI). Traditional
model-based deconvolution methods assume all fibers
in a given voxel are composed of the same biological
components and thus have identical water restriction
properties. In RSI, the tissue is modeled using a
spectrum of both oriented and non-oriented diffusion
components with different water restriction scales.
Both the volume fraction and 3D orientation of each
individual component can be derived in each voxel
using linear estimation methods. RSI may provide a
more complete characterization of tissue with
complex neuromorphologies. |
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