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Contextual Diffusion Image Post-processing Aids Clinical Applications

  • Vesna PrčkovskaEmail author
  • Magí Andorrà
  • Pablo Villoslada
  • Eloy Martinez-Heras
  • Remco Duits
  • David Fortin
  • Paulo Rodrigues
  • Maxime Descoteaux
Part of the Mathematics and Visualization book series (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.

Keywords

Multiple Sclerosis Diffusion Tensor Imaging Optic Radiation Rigid Body Motion Orientation Distribution Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vesna Prčkovska
    • 1
    Email author
  • Magí Andorrà
    • 2
  • Pablo Villoslada
    • 2
  • Eloy Martinez-Heras
    • 2
  • Remco Duits
    • 3
  • David Fortin
    • 4
  • Paulo Rodrigues
    • 5
  • Maxime Descoteaux
    • 4
  1. 1.Center for Neuroimmunology, Service of NeurologyHospital Clínic and Institut d’Investigació Biomèdica Augustí Pi I Sunyer (IDIBAPS)BarcelonaSpain
  2. 2.IDIBAPSBarcelonaSpain
  3. 3.IST/e, Eindhoven University of TechnologyEindhovenThe Netherlands
  4. 4.Université de SherbrookeSherbrookeCanada
  5. 5.Mint Labs S.L.BarcelonaSpain

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