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Population-averaged MRI atlases for automated image processing and assessments of lumbar paraspinal muscles

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Abstract

Purpose

Growing evidence suggests an association between lumbar paraspinal muscle degeneration and low back pain (LBP). Currently, time-consuming and laborious manual segmentations of paraspinal muscles are commonly performed on magnetic resonance imaging (MRI) axial scans. Automated image analysis algorithms can mitigate these drawbacks, but they often require individual MRIs to be aligned to a standard “reference” atlas. Such atlases are well established in automated neuroimaging analysis. Our aim was to create atlases of similar nature for automated paraspinal muscle measurements.

Methods

Lumbosacral T2-weighted MRIs were acquired from 117 patients who experienced LBP, stratified by gender and age group (30–39, 40–49, and 50–59 years old). Axial MRI slices of the L4–L5 and L5–S1 levels at mid-disc were obtained and aligned using group-wise linear and nonlinear image registration to produce a set of unbiased population-averaged atlases for lumbar paraspinal muscles.

Results

The resulting atlases represent the averaged morphology and MRI intensity features of the corresponding cohorts. Differences in paraspinal muscle shapes and fat infiltration levels with respect to gender and age can be visually identified from the population-averaged data from both linear and nonlinear registrations.

Conclusion

We constructed a set of population-averaged atlases for developing automated algorithms to help analyze paraspinal muscle morphometry from axial MRI scans. Such an advancement could greatly benefit the fields of paraspinal muscle and LBP research.

Graphical abstract

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Acknowledgement

This project was partly funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2015-04136.

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Authors and Affiliations

  1. Robarts Research Institute, Western University, 1151 Richmond Street North, London, ON, N6A 5B7, Canada

    Yiming Xiao

  2. PERFORM Centre, Concordia University, Montreal, Canada

    Maryse Fortin & Hassan Rivaz

  3. School of Physical Therapy, Western University, London, Canada

    Michele C. Battié

  4. Bone and Joint Institute, Western University, London, Canada

    Michele C. Battié

  5. Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada

    Hassan Rivaz

Authors
  1. Yiming Xiao
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  2. Maryse Fortin
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  3. Michele C. Battié
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  4. Hassan Rivaz
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Corresponding author

Correspondence to Yiming Xiao.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Xiao, Y., Fortin, M., Battié, M.C. et al. Population-averaged MRI atlases for automated image processing and assessments of lumbar paraspinal muscles. Eur Spine J 27, 2442–2448 (2018). https://doi.org/10.1007/s00586-018-5704-z

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  • DOI: https://doi.org/10.1007/s00586-018-5704-z

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