Traditional Posters: Diffusion & Perfusion - ismrm

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prior knowledge gives substantially more robust results. Our technique is fully automated and uses a reference tract to inform the process. 1680. Quantitative Comparison of Automatic and Manual Tract Segmentation Methods Susana Muñoz Maniega 1 , James D. Bridson 2 , Wei Jie Jensen Ang 2 , Paul A. Armitage 1 , Catherine Murray 3 , Alan J. Gow 3 , Mark E. Bastin 4 , Ian J. Deary 3 , Joanna M. Wardlaw 1 1 Clinical Neurosciences, University of Edinburgh, Edinburgh, United Kingdom; 2 Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom; 3 Psychology, University of Edinburgh, Edinburgh, United Kingdom; 4 Medical Physics, University of Edinburgh, Edinburgh, United Kingdom We compare probabilistic neighbourhood tractography (PNT), an automatic tract segmentation method, with a well accepted tractography method using manual seed placement and multiple region-of-interest (ROI) constraints. Tracts were segmented in the same data set using both methods and mean values of FA and MD compared. Mean differences between PNT and ROI methods were ≤10%, comparable with the reproducibility obtained when ROI are manually placed by different operators. PNT segmentation showed a reasonable agreement with the more conventional ROI tract segmentation method, with the advantage of removing operator dependency. 1681. A New Combined Distance Measure for the Clustering of Fiber Tracts in Diffusion Tensor Imaging (DTI) Christian Ros 1 , Daniel Güllmar 1 , Juergen R. Reichenbach 1 1 Medical Physics Group, Institute for Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany, Jena, Thuringia, Germany In recent years various fiber tractography methods have been evolved. Although these resulting tractograms offers plenty of information, they are rarely used in clinical routine due to the fact that processing is often time-consuming and an experienced operator is essential to obtain good results. To overcome this limitations cluster analysis can be employed to partition fiber tracts into clusters through comparison of tract-specific features or similarity measures. The aim of this study was to develop a new combined similarity measure that combines a shape based distance measure with other distance measures. 1682. Visualizing and Exploring Tractograms Via Two-Dimensional Connectivity Maps Radu Jianu 1 , Cagatay Demiralp 1 , David H. Laidlaw 1 1 Brown University, Providence, RI, United States We introduce a circular graph visualization of tract projections in a framework that uses two-dimensional map representations for exploring connectivity in the brain. Expert feedback indicates that it can be useful for understanding connectivity densities and configurations. 1683. Visualization of Intrarenal Water Transport by Diffusion Tensor Tractography Michael Pedersen 1 , Anders B. Lødrup 1 , Kristian Karstoft 1 , Eva A. Nielsen 2 , Mette K. Hagensen 2 , Peter A. Nielsen 2 , Andreas Stavropoulos 3 , Bente Jespersen 4 , Steffen Ringgaard 1 , Morten Smerup 2 1 MR Research Center, Aarhus University Hosptial, Aarhus, Denmark; 2 Institute of Clinical Medicine, Aarhus University Hosptial, Aarhus, Denmark; 3 Dept. of Periodontology, Aarhus University, Aarhus, Denmark; 4 Department of Nephrology, Aarhus University Hospital, Aarhus, Denmark The aim of this study is to investigate if DTI can be used for imaging the principal route of free water in the kidney, and we hypothesize that this route can act as an indirect representation of the segments of nephrons going centripetally from the renal parenchyma to the collecting ducts. The orientation of medullary diffusion anisotrophy was visualized using a proposed DTI tractography method 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 diffusionweighted 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. 1690. An Accelerated, Alternative Approach for Estimating Zero-Displacement Probability in Hybrid Diffusion Imaging A P. Hosseinbor 1 , J O. Fleming 2 , Y-C Wu 3 , A A. Samsonov 4 , A L. Alexander 1 Medical Physics, University of Wisconsin-Madison, Madison, WI, United States; 2 Neurology, University of Wisconsin-Madison; 3 Dartmouth College; 4 Radiology, University of Wisconsin-Madison In HYDI, Po is conventionally estimated by using signal measurements in all shells (Poall), which requires long scan time. However, the highest diffusion-weighting measurements are likely to contribute most heavily to restricted diffusion (RD) signal. Thus, an alternative, faster approach for characterizing RD would be to use signal measurements only in outermost shell (Poouter). In this work, we compare both Poall and Poouter approaches in NAWM from MS patients and WM in a control group. We show that both
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Traditional Posters: Diffusion & Perfusion - ismrm

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