TRADITIONAL POSTER - ismrm

Poster Sessions
MARDI
Hall B Thursday 13:30-15:30
1684. A Monte-Carlo Approach for Estimating White Matter Density in HARDI Diffusion Data
Parnesh Raniga 1 , Kerstin Pannek 2,3 , Jurgen Fripp 1 , David Raffelt 1 , Pierrick Bourgeat 1 , Oscar Acosta 1 ,
Donald Tournier 4 , Allan Connelly 4 , Stephen Rose 2,3 , Olivier Salvado 1
1 CSIRO Preventative Health National Research Flagship ICTC, The Australian e-Health Research Centre, Brisbane, Queensland,
Australia; 2 Centre for Magnetic Resonance, University of Queensland, Brisbane, Queensland, Australia; 3 UQ Centre for Clinical
Research, University of Queensland, Brisbane, Queensland, Australia; 4 Brain Research Institute, Melbourne, Victoria, Australia
The abstract is about using visitation maps to perform quantitative analysis.
1685. On the Behavior of DTI and Q-Ball Derived Anisotropy Indices
Klaus H. Fritzsche 1 , Bram Stieltjes 2 , Frederik B. Laun 3 , Hans-Peter Meinzer 1
1 Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, B-W, Germany; 2 Division of
Radiology, German Cancer Research Center; 3 Division of Medical Physics, German Cancer Research Center
Anisotropy indices in diffusion imaging have never been systematically analyzed under conditions of heterogeneous fiber configurations. Furthermore, q-
ball imaging indices have so far not been evaluated with respect to accuracy, precision, b-value dependency and contrast-to-noise ratio (CNR). This study
performed a systematic analysis using Monte Carlo simulations and measurements in crossing fiber phantoms. The GFA (reconstructed with solid angle
consideration) showed the lowest dependency on b-value and the best results regarding accuracy and precision. Its behavior in crossing fiber voxels was also
preferable. Main drawback was its low CNR, especially in low anisotropy fibers.
1686. Analytical Q-Ball Imaging with Optimal λ-Regularization
Maxime Descoteaux 1 , Cheng Guan Koay 2 , Peter J. Basser 2 , Rachid Deriche 3
1 Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada; 2 National Institute of Child Health and Human
Development, Bethesda, MD, United States; 3 INRIA Sophia Antipolis - Méditerranée, Sophia Antipolis, France
We present analytical q-ball imaging with optimal Generalized Cross Validation (GCV)-based regularization. The method is the optimal extension of the
standard analytical q-ball imaging, normally implemented using a fixed regularization λ = 0.006. QBI with optimal λ shows a distinct advantage in
generalized fractional anisotropy (GFA) computation when the underlying structure is complex and in single fiber parts of real data.
1687. A More Accurate and B-Value Independent Estimation of Diffusion Parameters Using Diffusion
Kurtosis Imaging
Jelle Veraart 1 , Wim Van Hecke 2,3 , Dirk Poot 1 , Ines Blockx 4 , Annemie Van Der Linden 4 , Marleen Verhoye 4 ,
Jan Sijbers 1
1 Vision Lab, University of Antwerp, Antwerp, Belgium; 2 Department of Radiology, Antwerp University Hospital, Antwerp, Belgium;
3 Department of Radiology, University Hospitals of the Catholic University of Leuven, Leuven, Belgium; 4 Bio Imaging Lab,
University of Antwerp, Antwerp, Belgium
Due to the presence of complex cellular microstructures in the brains’ white matter, the diffusion weighted signal attenuation with respect to the b-value can
not accurately be approximated by the monoexponential function assumed by DTI. Because of this, the estimation of the diffusion coefficient and the
associated diffusion parameters depend on the b-value of the acquisition. The recently proposed higher order DKI model fits the signal attenuation more
properly as a result of which, as demonstrated in this study, a more accurate estimation of the diffusion parameters is obtained. In addition the parameter
estimation appears b-value independent.
1688. Anomalous Diffusion Tensor Imaging
Matt G. Hall 1 , Thomas Richard Barrick 2
1 Dept of Computer Science, University College London, London, United Kingdom; 2 Centre for Clinical Neuroscience, Division of
Cardiac & Vasculas Sciences, St Georges, University of London, London, United Kingdom
The theory of anomalous diffusion applied to diffusion imaging predicts a stretched-exponential form for the decay of diffusion-weighted signal with b-
value. We generalise this to consider diretional anisotropy of the parameters of the stretched-exponential form. The resulting technique (anomalous diffusion
tensor imaging) provides estimates of tensors describing diffusivity and tissue heteroegeneity in each scan voxel. We apprly the technique to healthy in vivo
data and use the resulting tensors to infer tissue microstructure perform streamline tractography in the corpus callosum.
1689. Spectral Decomposition of a 4-Rank Tensor and Applications to Generalised Diffusion Tensor Imaging
Marta Morgado Correia 1,2 , Guy B. Williams 2
1 MRC Cognition and Brain Sciences Unit, Cambridge, Cambridgeshire, United Kingdom; 2 Wolfson Brain Imaging Centre,
Cambridge, Cambridgeshire, United Kingdom
In this work we show how spectral decomposition of a 4-rank generalised diffusion tensor can be used to characterise brain structure, including the definition
of two metrics of anisotropy that do not depend on the arbitrary choice of a normalising function and its parameters.