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A method for fast automated assessment of the magnetic resonance parkinsonism index

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Abstract

In patients with parkinsonian syndromes, and particularly during the early stages of the clinical disease, differentiating between idiopathic Parkinson’s disease and progressive supranuclear palsy is challenging. Imaging plays an important role in early diagnosis, and the magnetic resonance parkinsonism index was shown to reliably differentiate between the two entities. Calculation of the index is a time-consuming process. We developed an algorithm allowing its automatic calculation based on 3D T1-weighted images, producing additional color-coded images for verification.

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Funding

No funding was received for this study.

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Authors and Affiliations

  1. Division of Radiology, Geneva University Hospitals, Ch. du Pont-Bochet 3, 1226 Thonex, Geneva, Switzerland

    Max Scheffler

  2. Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland

    Bénédicte Maréchal

  3. LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Bénédicte Maréchal

  4. Division of Neuroradiology, Geneva University Hospitals and University of Geneva, Geneva, Switzerland

    José Boto, Karl-Olof Lövblad & Maria Isabel Vargas

Authors
  1. Max Scheffler
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  2. Bénédicte Maréchal
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  3. José Boto
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  4. Karl-Olof Lövblad
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  5. Maria Isabel Vargas
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Corresponding author

Correspondence to Max Scheffler.

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The authors declare that they have no conflict of interest.

Ethical approval

Institutional review board approval (number 2016-01-821) was obtained for this study. All procedures performed involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Individual written consent was waived.

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Scheffler, M., Maréchal, B., Boto, J. et al. A method for fast automated assessment of the magnetic resonance parkinsonism index. Neuroradiology 62, 747–751 (2020). https://doi.org/10.1007/s00234-020-02380-5

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  • DOI: https://doi.org/10.1007/s00234-020-02380-5

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