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@ARTICLE{Zimmermann:862505,
      author       = {Zimmermann, Markus and Oros-Peusquens, Ana-Maria and
                      Iordanishvili, Elene and Shin, Seonyeong and Yun, Seong Dae
                      and Abbas, Zaheer and Shah, N. J.},
      title        = {{M}ulti-exponential {R}elaxometry using l1-regularized
                      {I}terative {NNLS} ({MERLIN}) with {A}pplication to {M}yelin
                      {W}ater {F}raction {I}maging},
      journal      = {IEEE transactions on medical imaging},
      volume       = {38},
      number       = {11},
      issn         = {1558-254X},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2019-02809},
      pages        = {2676 - 2686},
      year         = {2019},
      abstract     = {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.},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      ddc          = {620},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {573 - Neuroimaging (POF3-573)},
      pid          = {G:(DE-HGF)POF3-573},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30990178},
      UT           = {WOS:000494433300017},
      doi          = {10.1109/TMI.2019.2910386},
      url          = {https://juser.fz-juelich.de/record/862505},
}