Delineating white matter structure in diffusion tensor MRI with anisotropy creases.
ABSTRACT Geometric models of white matter architecture play an increasing role in neuroscientific applications of diffusion tensor imaging, and the most popular method for building them is fiber tractography. For some analysis tasks, however, a compelling alternative may be found in the first and second derivatives of diffusion anisotropy. We extend to tensor fields the notion from classical computer vision of ridges and valleys, and define anisotropy creases as features of locally extremal tensor anisotropy. Mathematically, these are the loci where the gradient of anisotropy is orthogonal to one or more eigenvectors of its Hessian. We propose that anisotropy creases provide a basis for extracting a skeleton of the major white matter pathways, in that ridges of anisotropy coincide with interiors of fiber tracts, and valleys of anisotropy coincide with the interfaces between adjacent but distinctly oriented tracts. The crease extraction algorithm we present generates high-quality polygonal models of crease surfaces, which are further simplified by connected-component analysis. We demonstrate anisotropy creases on measured diffusion MRI data, and visualize them in combination with tractography to confirm their anatomic relevance.
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ABSTRACT: The structure of fiber tracts in DT-MRI data presents a challenging problem for visualization and analysis. We derive visualization of such traces from a local coherence measure and achieve much improved visual segmentation. We introduce a coherence measure defined for fiber tracts. This quantitative assessment is based on infinitesimal deviations of neighboring tracts and allows identification and segmentation of coherent fiber regions. We use a hardware-accelerated implementation to achieve interactive visualization on slices and provide several approaches to visualize coherence information. Furthermore, we enhance existing techniques by combining them with coherence. We demonstrate our method on both a canine heart, where the myocardial structure is visualized, and a human brain, where we achieve detailed visualization of major and minor fiber bundles in a quality similar to and exceeding fiber clustering approaches. Our approach allows detailed and fast visualization of important anatomical structures in DT-MRI data sets.International Journal of Computer Assisted Radiology and Surgery 03/2010; 5(2):125-31. · 1.48 Impact Factor
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ABSTRACT: We derive herein first and second-order differential operators for detecting structure in diffusion tensor MRI (DTI). Unlike existing methods, we are able to generate full first and second-order differentials without dimensionality reduction and while respecting the underlying manifold of the data. Further, we extend corner and curvature feature detectors to DTI using our differential operators. Results using the feature detectors on diffusion tensor MR images show the ability to highlight structure within the image that existing methods cannot.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 2):90-7.
Conference Proceeding: New Scalar Measures for Diffusion-Weighted MRI Visualization.Advances in Visual Computing, 5th International Symposium, ISVC 2009, Las Vegas, NV, USA, November 30 - December 2, 2009, Proceedings, Part I; 01/2009