作者
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
发表日期
2021/4/1
期刊
Medical Image Analysis
卷号
69
页码范围
101959
出版商
Elsevier
简介
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 …
引用总数
20212022202320243974
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