• Yiming Xiao, Maryse Fortin, Michele C Battié, and Hassan Rivaz.
• Robarts Research Institute, Western University, 1151 Richmond Street North, London, ON, N6A 5B7, Canada. yxiao286@uwo.ca.
• Eur Spine J. 2018 Oct 1; 27 (10): 2442-2448.

PurposeGrowing 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.MethodsLumbosacral 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.ResultsThe 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.ConclusionWe 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. These slides can be retrieved under Electronic Supplementary Material.

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