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

Erick Jorge Canales-Rodríguez*, Marco Pizzolato, Gian Franco Piredda, Tom Hilbert, Nicolas Kunz, Caroline Pot, Thomas Yu, Raymond Salvador, Edith Pomarol-Clotet, Tobias Kober, Jean Philippe Thiran, Alessandro Daducci

*Corresponding author for this work

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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.

Original languageEnglish
Article number101959
JournalMedical Image Analysis
Volume69
Number of pages13
ISSN1361-8415
DOIs
Publication statusPublished - Apr 2021

Bibliographical note

Funding Information:
This work was supported by the Instituto de Salud Carlos III, Spain (Research Project PI15/00277 and Sara Borrell Contract CD18/00029 to E.J.C.-R; Research Projects PI14/01151 and PI18/00877 and Miguel Servet Research Contract CP07/00048 to R.S.; Research Project PI14/01148 and Miguel Servet Research Contract CPII16/00018 to E.P.-C.) and by the Catalonian Government (Research Project 2017-SGR-01271 to E.P.-C). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 754462 (M.P.). T.Y. is supported by European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie project TRABIT agreement No 765148. A.D. was supported by the Rita Levi Montalcini Programme of the Italian Ministry of Education, University and Research (MIUR). E.J.C-R. was supported by the Facult? de Biologie et de m?decine of the Lausanne University Hospital Center (CHUV) and by the Swiss National Science Foundation, Ambizione grant PZ00P2_185814.

Funding Information:
This work was supported by the Instituto de Salud Carlos III, Spain (Research Project PI15/00277 and Sara Borrell Contract CD18/00029 to E.J.C.-R; Research Projects PI14/01151 and PI18/00877 and Miguel Servet Research Contract CP07/00048 to R.S.; Research Project PI14/01148 and Miguel Servet Research Contract CPII16/00018 to E.P.-C.) and by the Catalonian Government (Research Project 2017-SGR-01271 to E.P.-C). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754462 (M.P.). T.Y. is supported by European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie project TRABIT agreement No 765148. A.D. was supported by the Rita Levi Montalcini Programme of the Italian Ministry of Education, University and Research (MIUR). E.J.C-R. was supported by the Faculté de Biologie et de médecine of the Lausanne University Hospital Center (CHUV) and by the Swiss National Science Foundation, Ambizione grant PZ00P2_185814.

Publisher Copyright:
© 2021

Keywords

  • Myelin water imaging
  • Non-negative least squares
  • T relaxometry
  • Tikhonov regularization
  • Tissue microstructure

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