Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers

Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used qualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T<sub>1</sub>, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T<sub>1</sub> <sup>MP2RAGE</sup>, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient - ICC ≃ 1 and within-subject coefficient of variations - wCV < 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T<sub>1</sub> <sup>MTsat</sup>exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.

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Publication Details

Journal
Imaging Neuroscience
Published
2024-12-10
DOI
https://doi.org/10.1162/imag_a_00409
Citations
6
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.43
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article

Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers

Eva Alonso‐Ortiz, Mathieu Boudreau, Kiril Zelenkovski, Julien Cohen‐Adad et al.
6 citations
Imaging Neuroscience
Advanced MRI Techniques and Applications
2.43
article

Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers

Eva Alonso‐Ortiz, Mathieu Boudreau, Kiril Zelenkovski, Julien Cohen‐Adad, Basile Pinsard, Arnaud Boré, Julie Boyle, Agâh Karakuzu, Lune Bellec
article en
6 citations

Abstract

Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used qualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T<sub>1</sub>, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T<sub>1</sub> <sup>MP2RAGE</sup>, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient - ICC ≃ 1 and within-subject coefficient of variations - wCV < 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T<sub>1</sub> <sup>MTsat</sup>exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.

Imaging NeuroscienceVol. 3
Centre Hospitalier Universitaire Sainte-Justine (CA), Institut Universitaire de Gériatrie de Montréal (CA), Mila - Quebec Artificial Intelligence Institute (CA), Polytechnique Montréal (CA), Université de Montréal (CA)
Openalex Percentile: Top 10%
Advanced MRI Techniques and Applications
2.43
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