Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions

• Published in: Frontiers in radiology Vol. 2; p. 930666
• Main Authors: Fischi-Gomez, Elda, Girard, Gabriel, Koch, Philipp J., Yu, Thomas, Pizzolato, Marco, Brügger, Julia, Piredda, Gian Franco, Hilbert, Tom, Cadic-Melchior, Andéol G., Beanato, Elena, Park, Chang-Hyun, Morishita, Takuya, Wessel, Maximilian J., Schiavi, Simona, Daducci, Alessandro, Kober, Tobias, Canales-Rodríguez, Erick J., Hummel, Friedhelm C., Thiran, Jean-Philippe
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 28-07-2022
• Subjects: MRI; multi-echo; quantitative MRI; Radiology; relaxometry; reproducibility; variability
• ISSN: 2673-8740; 2673-8740
• DOI: 10.3389/fradi.2022.930666

Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo
T
2
relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific
T
2
relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space (
T
2
I
E
) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run
T
2
relaxometry dataset. To this end, we evaluated three different techniques for estimating the
T
2
spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that
T
2
I
E
is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92.