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Tensor-based morphometry of fibrous structures with application to human brain white matter

Zhang, H; Yushkevich, PA; Rueckert, D; Gee, JC; (2009) Tensor-based morphometry of fibrous structures with application to human brain white matter. In: Yang, GZ and Hawkes, D and Rueckert, D and Noble, A and Taylor, C, (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part II. (pp. 466 - 473). Springer Berlin Heidelberg: Berlin, Germany.

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Abstract

Tensor-based morphometry (TBM) is a powerful approach for examining shape changes in anatomy both across populations and in time. Our work extends the standard TBM for quantifying local volumetric changes to establish both rich and intuitive descriptors of shape changes in fibrous structures. It leverages the data from diffusion tensor imaging to determine local spatial configuration of fibrous structures and combines this information with spatial transformations derived from image registration to quantify fibrous structure-specific changes, such as local changes in fiber length and in thickness of fiber bundles. In this paper, we describe the theoretical framework of our approach in detail and illustrate its application to study brain white matter. Our results show that additional insights can be gained with the proposed analysis.

Type: Proceedings paper
Title: Tensor-based morphometry of fibrous structures with application to human brain white matter
Location: Germany
ISBN-13: 9783642042706
DOI: 10.1007/978-3-642-04271-3_57
Publisher version: http://dx.doi.org/10.1007/978-3-642-04271-3_57
Language: English
Keywords: Algorithms, Brain, Diffusion Tensor Imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Information Storage and Retrieval, Nerve Fibers, Myelinated, Reproducibility of Results, Sensitivity and Specificity
UCL classification: UCL > School of BEAMS > Faculty of Engineering Science
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/1306724
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