Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher.

Centre de Recherche du CHU Sainte Justine Université de Montréal Montreal Canada

Department of Neurology Faculty of Medicine and Dentistry Palacký University Olomouc Olomouc Czechia

Department of Neurosurgery Faculty of Medicine and Dentistry Palacký University Olomouc Olomouc Czechia

Department of Radiology and Nuclear Medicine University Hospital Brno and Masaryk University Brno Czechia

Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal Canada

Mila Quebec AI Institute Montreal Canada

NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal Canada

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