Hauptseite > Publikationsdatenbank > Multi-exponential Relaxometry using l1-regularized Iterative NNLS (MERLIN) with Application to Myelin Water Fraction Imaging > print

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100 1 _ |a Zimmermann, Markus
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245 _ _ |a Multi-exponential Relaxometry using l1-regularized Iterative NNLS (MERLIN) with Application to Myelin Water Fraction Imaging
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520 _ _ |a A new parameter estimation algorithm, MERLIN, is presented for accurate and robust multi-exponential relaxometry using magnetic resonance imaging, a tool that can provide valuable insight into the tissue microstructure of the brain. Multi-exponential relaxometry is used to analyze the myelin water fraction and can help to detect related diseases. However, the underlying problem is ill-conditioned, and as such, is extremely sensitive to noise and measurement imperfections, which can lead to less precise and more biased parameter estimates. MERLIN is a fully automated, multi-voxel approach that incorporates state-of-the-art $\ell _{1}$ -regularization to enforce sparsity and spatial consistency of the estimated distributions. The proposed method is validated in simulations and in vivo experiments, using a multi-echo gradient-echo (MEGE) sequence at 3 T. MERLIN is compared to the conventional single-voxel $\ell _{2}$ -regularized NNLS (rNNLS) and a multi-voxel extension with spatial priors (rNNLS + SP), where it consistently showed lower root mean squared errors of up to 70 percent for all parameters of interest in these simulations.
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700 1 _ |a Oros-Peusquens, Ana-Maria
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700 1 _ |a Iordanishvili, Elene
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700 1 _ |a Shin, Seonyeong
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700 1 _ |a Yun, Seong Dae
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700 1 _ |a Abbas, Zaheer
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700 1 _ |a Shah, N. J.
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