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Contextual Diffusion Image Post-processing Aids Clinical Applications

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Visualization and Processing of Higher Order Descriptors for Multi-Valued Data

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Diffusion weighted magnetic resonance imaging ( dMRI) and tractography have shown great potential for the investigation of the white mater architecture in-vivo, especially with the recent advancements by using higher order techniques to model the data. Many clinical applications ranging from neurodegenerative disorders, psychiatric disorders as well as pre-surgical planning employ diffusion imaging-based analysis as an addition to conventional MRI imaging. However, despite the promising outlook, dMRI tractography confronts many challenges that complicate its use in everyday clinical practice. Some of these challenges are low test-retest accuracy, poor quantification of tracts size, poor understanding of the biological basis of the dMRI parameters, inaccuracies in the geometry of the reconstructed streamlines (especially in complex areas with curvature, bifurcations, fanning, crossings), poor alignment with the neighboring diffusion profiles, among others. Recently developed contextual processing techniques including the one presented in this work, for enhancement and well-posed geometric sharpening, have shown to result in sharper and better aligned diffusion profiles. In this paper, we present a possibility in enabling HARDI tractography on the data acquired under limited diffusion tensor imaging (DTI) conditions and modeled by DTI. We enhance local features from the DTI field using operators that take ‘context’ information into account. Moreover, we demonstrate the potential of the contextual processing techniques in two important clinical applications: enhancing the streamlines in data acquired from patients with Multiple Sclerosis (MS) and pre-surgical planning for tumor resection. For the latter, we explore the possibilities of using this framework for more accurate neurosurgical planning and evaluate our findings with a feedback from a neurosurgeon.

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Notes

  1. 1.

    The coupled space of positions and orientations \(\mathbb{R}^{3} \rtimes S^{2}\) is formally defined as \(\mathbb{R}^{3} \rtimes S^{2}:= \mathbb{R}^{3} \rtimes SO(3)/(\{0\} \times SO(2))\).

  2. 2.

    https://github.com/scilus/fibernavigator.

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Acknowledgements

This work was supported by the FP7 Marie Curie Intra-European Fellowship, project acronym: ConnectMS, project number: 328060. Moreover, the research leading to the results of this article has received funding from the European Research Council under the European Community’s 7th Framework Program (FP7/2014) ERC grant agreement no. 335555.

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Authors and Affiliations

  1. Center for Neuroimmunology, Service of Neurology, Hospital Clínic and Institut d’Investigació Biomèdica Augustí Pi I Sunyer (IDIBAPS), Barcelona, Spain

    Vesna Prčkovska

  2. IDIBAPS, Barcelona, Spain

    Magí Andorrà, Pablo Villoslada & Eloy Martinez-Heras

  3. IST/e, Eindhoven University of Technology, Den Dolech 2, 5600 MB, Eindhoven, The Netherlands

    Remco Duits

  4. Université de Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada

    David Fortin & Maxime Descoteaux

  5. Mint Labs S.L., Barcelona, Spain

    Paulo Rodrigues

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  1. Vesna Prčkovska
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  2. Magí Andorrà
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  3. Pablo Villoslada
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  4. Eloy Martinez-Heras
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  5. Remco Duits
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  6. David Fortin
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  7. Paulo Rodrigues
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  8. Maxime Descoteaux
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Correspondence to Vesna Prčkovska .

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Editors and Affiliations

  1. Linköping University, Norrköping, Sweden

    Ingrid Hotz

  2. Visualization and Medical Image Analysis, University of Bonn, Bonn, Germany

    Thomas Schultz

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Prčkovska, V. et al. (2015). Contextual Diffusion Image Post-processing Aids Clinical Applications. In: Hotz, I., Schultz, T. (eds) Visualization and Processing of Higher Order Descriptors for Multi-Valued Data. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-15090-1_18

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