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Patch-Based DTI Grading: Application to Alzheimer’s Disease Classification

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Book cover Patch-Based Techniques in Medical Imaging (Patch-MI 2016)

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Abstract

Early diagnosis is one of the most important challenges related to Alzheimer’s disease (AD). To address this issue, numerous studies proposed biomarkers based on anatomical MRI. Among them, patch-based grading demonstrated state-of-the-art results when applied to T1-weighted MRI. In this work, we propose to use a similar framework on different diffusion parameters extracted from DTI. We also propose to use a fast patch-based search strategy to provide novel biomarkers for the early detection of AD. We intensively compare our new grading-based DTI features with basic MRI/DTI biomarkers and evaluate our method within a cross validation classification framework. Finally, we demonstrate that the proposed biomarkers obtain competitive results for the identification of the different stages of AD.

The Alzheimer’s Disease Neuroimaging Initiative–Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Notes

  1. 1.

    http://adni.loni.ucla.edu.

  2. 2.

    https://adni.loni.usc.edu/wp-content/uploads/2010/05/ADNI2_GE_3T_22.0_T2.pdf.

  3. 3.

    http://volbrain.upv.es.

  4. 4.

    http://nipy.org/dipy/.

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Acknowledgement

This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project “Défi imag’In”. This research was also supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad, NIH grants P30AG010129, K01 AG030514 and the Dana Foundation.

Author information

Authors and Affiliations

  1. University of Bordeaux, LaBRI, UMR 5800 PICTURA, 33400, Talence, France

    Kilian Hett, Vinh-Thong Ta, Rémi Giraud, Mary Mondino & Pierrick Coupé

  2. CNRS, LaBRI, UMR 5800, PICTURA, 33400, Talence, France

    Kilian Hett, Vinh-Thong Ta, Rémi Giraud, Mary Mondino & Pierrick Coupé

  3. Bordeaux INP, LaBRI, UMR 5800, PICTURA, 33600, Pessac, France

    Vinh-Thong Ta

  4. University of Bordeaux, IMB, UMR 5251, 33400, Talence, France

    Rémi Giraud

  5. CNRS, IMB, UMR 5251, 33400, Talence, France

    Rémi Giraud

  6. Universitat Politècnia de València, ITACA, 46022, Valencia, Spain

    José V. Manjón

Authors
  1. Kilian Hett
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  2. Vinh-Thong Ta
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  3. Rémi Giraud
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  4. Mary Mondino
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  5. José V. Manjón
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  6. Pierrick Coupé
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Consortia

Alzheimer’s Disease Neuroimaging Initiative

Corresponding author

Correspondence to Kilian Hett .

Editor information

Editors and Affiliations

  1. University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Guorong Wu

  2. Bordeaux University , Bordeaux, France

    Pierrick Coupé

  3. Siemens Healthcare , Malvern, Pennsylvania, USA

    Yiqiang Zhan

  4. College of Charleston , Charleston, South Carolina, USA

    Brent C. Munsell

  5. Imperial College London , London, United Kingdom

    Daniel Rueckert

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Hett, K. et al. (2016). Patch-Based DTI Grading: Application to Alzheimer’s Disease Classification. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B., Rueckert, D. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2016. Lecture Notes in Computer Science(), vol 9993. Springer, Cham. https://doi.org/10.1007/978-3-319-47118-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-47118-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47117-4

  • Online ISBN: 978-3-319-47118-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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