%0 Journal Article
%A Zimmermann, Markus
%A Oros-Peusquens, Ana-Maria
%A Iordanishvili, Elene
%A Shin, Seonyeong
%A Yun, Seong Dae
%A Abbas, Zaheer
%A Shah, N. J.
%T Multi-exponential Relaxometry using l1-regularized Iterative NNLS (MERLIN) with Application to Myelin Water Fraction Imaging
%J IEEE transactions on medical imaging
%V 38
%N 11
%@ 1558-254X
%C New York, NY
%I IEEE
%M FZJ-2019-02809
%P  2676 - 2686
%D 2019
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:30990178
%U <Go to ISI:>//WOS:000494433300017
%R 10.1109/TMI.2019.2910386
%U https://juser.fz-juelich.de/record/862505