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Comparison of non-parametric T2 relaxometry methods for myelin water quantification

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Canales-Rodríguez Erick Jorge, Pizzolato Marco, Piredda Gian Franco, Hilbert Tom, Kunz Nicolas, Pot Caroline, Yu Thomas, Salvador Raymond, Pomarol-Clotet Edith, Kober Tobias, Thiran Jean-Philippe, Daducci Alessandro,
Project Exploring brain communication pathways by combining diffusion based quantitative structural connectivity and EEG source imaging : application to physiological and epileptic networks
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Original article (peer-reviewed)

Journal Medical Image Analysis
Volume (Issue) 69
Page(s) 101959 - 101959
Title of proceedings Medical Image Analysis
DOI 10.1016/j.media.2021.101959

Open Access

URL https://doi.org/10.1016/j.dib.2021.107429
Type of Open Access Publisher (Gold Open Access)

Abstract

Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is the conventional method for estimating smooth T2 distributions. Despite the improved estimation provided by this method compared to non-regularized NNLS, the solution is still sensitive to the underlying noise and the regularization weight. This is especially relevant for clinically achievable signal-to-noise ratios. In the literature of inverse problems, various well-established approaches to promote smooth solutions, including first-order and second-order Tikhonov regularization, and different criteria for estimating the regularization weight have been proposed, such as L-curve, Generalized Cross-Validation, and Chi-square residual fitting. However, quantitative comparisons between the available reconstruction methods for computing the T2 distribution, and between different approaches for selecting the optimal regularization weight, are lacking. In this study, we implemented and evaluated ten reconstruction algorithms, resulting from the individual combinations of three penalty terms with three criteria to estimate the regularization weight, plus non-regularized NNLS. Their performance was evaluated both in simulated data and real brain MRI data acquired from healthy volunteers through a scan-rescan repeatability analysis. Our findings demonstrate the need for regularization. As a result of this work, we provide a list of recommendations for selecting the optimal reconstruction algorithms based on the acquired data. Moreover, the implemented methods were packaged in a freely distributed toolbox to promote reproducible research, and to facilitate further research and the use of this promising quantitative technique in clinical practice.
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