29 research outputs found
Dagstuhl Annual Report January - December 2011
The International Conference and Research Center for Computer Science is a non-profit organization. Its objective is to promote world-class research in computer science and to host research seminars which enable new ideas to be showcased, problems to be discussed and the course to be set for future development in this field. The work being done to run this informatics center is documented in this report for the business year 2011
Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution
We propose two strategies to improve the quality of tractography results
computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both
methods are based on the same PDE framework, defined in the coupled space of
positions and orientations, associated with a stochastic process describing the
enhancement of elongated structures while preserving crossing structures. In
the first method we use the enhancement PDE for contextual regularization of a
fiber orientation distribution (FOD) that is obtained on individual voxels from
high angular resolution diffusion imaging (HARDI) data via constrained
spherical deconvolution (CSD). Thereby we improve the FOD as input for
subsequent tractography. Secondly, we introduce the fiber to bundle coherence
(FBC), a measure for quantification of fiber alignment. The FBC is computed
from a tractography result using the same PDE framework and provides a
criterion for removing the spurious fibers. We validate the proposed
combination of CSD and enhancement on phantom data and on human data, acquired
with different scanning protocols. On the phantom data we find that PDE
enhancements improve both local metrics and global metrics of tractography
results, compared to CSD without enhancements. On the human data we show that
the enhancements allow for a better reconstruction of crossing fiber bundles
and they reduce the variability of the tractography output with respect to the
acquisition parameters. Finally, we show that both the enhancement of the FODs
and the use of the FBC measure on the tractography improve the stability with
respect to different stochastic realizations of probabilistic tractography.
This is shown in a clinical application: the reconstruction of the optic
radiation for epilepsy surgery planning
Accelerating the Computation of Tensor -eigenvalues
Efficient solvers for tensor eigenvalue problems are important tools for the
analysis of higher-order data sets. Here we introduce, analyze and demonstrate
an extrapolation method to accelerate the widely used shifted symmetric higher
order power method for tensor -eigenvalue problems. We analyze the
asymptotic convergence of the method, determining the range of extrapolation
parameters that induce acceleration, as well as the parameter that gives the
optimal convergence rate. We then introduce an automated method to dynamically
approximate the optimal parameter, and demonstrate it's efficiency when the
base iteration is run with either static or adaptively set shifts. Our
numerical results on both even and odd order tensors demonstrate the theory and
show we achieve our theoretically predicted acceleration.Comment: 22 pages, 8 figures, 4 table
Computational Brain Connectivity Mapping: A Core Health and Scientific Challenge
International audienceOne third of the burden of all the diseases in Europe is due to problems caused by diseases affecting brain. Although exceptional progress have been obtained for exploring the brain during the past decades, it is still terra-incognita and calls for specific efforts in research to better understand its architecture and functioning. To take up this great challenge of modern science and to solve the limited view of the brain provided just by one imaging modality, this article advocates the idea developed in my research group of a global approach involving new generation of models for brain connectivity mapping and strong interactions between structural and functional connectivities. Capitalizing on the strengths of integrated and complementary non invasive imaging modalities such as diffusion Magnetic Resonance Imaging (dMRI) and Electro & Magneto-Encephalography (EEG & MEG) will contribute to achieve new frontiers for identifying and characterizing structural and functional brain connectivities and to provide a detailed mapping of the brain connectivity, both in space and time. Thus leading to an added clinical value for high impact diseases with new perspectives in computational neuro-imaging and cognitive neuroscience