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Usability and Potential of Geostatistics for Spatial Discrimination of Multiple Sclerosis Lesion Patterns

Authors

  • Robert Marschallinger PhD,

    1. Interdisciplinary Department of Geoinformatics, University of Salzburg, Austria
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    • Both authors contributed equally to the study. Funding Sources: The work has been financed by the Austrian Academy of Sciences and Paracelsus Medical University, Salzburg.

  • Stefan M. Golaszewski MD,

    1. Department of Neurology, Paracelsus Medical University, Christian-Doppler-Klinik, Salzburger Landeskliniken, Salzburg, Austria
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    • Both authors contributed equally to the study. Funding Sources: The work has been financed by the Austrian Academy of Sciences and Paracelsus Medical University, Salzburg.

  • Alexander B. Kunz MD,

    Corresponding author
    1. Department of Neurology, Paracelsus Medical University, Christian-Doppler-Klinik, Salzburger Landeskliniken, Salzburg, Austria
    • Correspondence: Address correspondence to Dr. Alexander Baden Kunz, Department of Neurology, Paracelsus Medical University and Salzburger Landesklinken, Christian-Doppler-Klinik, Ignaz-Harrer-Straße 79, 5020 Salzburg, Austria. E-mail: a.kunz@salk.at.

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  • Martin Kronbichler PhD,

    1. Department of Psychology, University of Salzburg, Austria
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  • Gunther Ladurner MD,

    1. Department of Neurology, Paracelsus Medical University, Christian-Doppler-Klinik, Salzburger Landeskliniken, Salzburg, Austria
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  • Peter Hofmann PhD,

    1. Interdisciplinary Department of Geoinformatics, University of Salzburg, Austria
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  • Eugen Trinka MD,

    1. Department of Neurology, Paracelsus Medical University, Christian-Doppler-Klinik, Salzburger Landeskliniken, Salzburg, Austria
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  • Mark McCoy MD,

    1. Division of Neuroradiology and MRI, Paracelsus Medical University, Christian-Doppler-Klinik, Salzburger Landeskliniken, Salzburg, Austria
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  • Jörg Kraus MD

    1. Department of Neurology, Paracelsus Medical University, Christian-Doppler-Klinik, Salzburger Landeskliniken, Salzburg, Austria
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ABSTRACT

BACKGROUND AND PURPOSE

In multiple sclerosis (MS) the individual disease courses are very heterogeneous among patients and biomarkers for setting the diagnosis and the estimation of the prognosis for individual patients would be very helpful. For this purpose, we are developing a multidisciplinary method and workflow for the quantitative, spatial, and spatiotemporal analysis and characterization of MS lesion patterns from MRI with geostatistics.

METHODS

We worked on a small data set involving three synthetic and three real-world MS lesion patterns, covering a wide range of possible MS lesion configurations. After brain normalization, MS lesions were extracted and the resulting binary 3-dimensional models of MS lesion patterns were subject to geostatistical indicator variography in three orthogonal directions.

RESULTS

By applying geostatistical indicator variography, we were able to describe the 3-dimensional spatial structure of MS lesion patterns in a standardized manner. Fitting a model function to the empirical variograms, spatial characteristics of the MS lesion patterns could be expressed and quantified by two parameters. An orthogonal plot of these parameters enabled a well-arranged comparison of the involved MS lesion patterns.

CONCLUSIONS

This method in development is a promising candidate to complement standard image-based statistics by incorporating spatial quantification. The work flow is generic and not limited to analyzing MS lesion patterns. It can be completely automated for the screening of radiological archives.

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