TY  - JOUR
AU  - Zimmermann, Markus
AU  - Oros-Peusquens, Ana-Maria
AU  - Iordanishvili, Elene
AU  - Shin, Seonyeong
AU  - Yun, Seong Dae
AU  - Abbas, Zaheer
AU  - Shah, N. J.
TI  - Multi-exponential Relaxometry using l1-regularized Iterative NNLS (MERLIN) with Application to Myelin Water Fraction Imaging
JO  - IEEE transactions on medical imaging
VL  - 38
IS  - 11
SN  - 1558-254X
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2019-02809
SP  -  2676 - 2686
PY  - 2019
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:30990178
UR  - <Go to ISI:>//WOS:000494433300017
DO  - DOI:10.1109/TMI.2019.2910386
UR  - https://juser.fz-juelich.de/record/862505
ER  -