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@ARTICLE{Emmerich:169968,
      author       = {J. Emmerich$^*$ and P. Bachert$^*$ and M. E. Ladd$^*$ and
                      S. Straub$^*$},
      title        = {{O}n the separation of susceptibility sources in
                      quantitative susceptibility mapping: {T}heory and phantom
                      validation with an in vivo application to multiple sclerosis
                      lesions of different age.},
      journal      = {Journal of magnetic resonance},
      volume       = {330},
      issn         = {1090-7807},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2021-01668},
      pages        = {107033},
      year         = {2021},
      note         = {#EA:E020#LA:E020#},
      abstract     = {In biological tissue, phase contrast is determined by
                      multiple substances such as iron, myelin or calcifications.
                      Often, these substances occur co-located within the same
                      measurement volume. However, quantitative susceptibility
                      mapping can solely measure the average susceptibility per
                      voxel. To provide new insight in disease progression and
                      mechanisms in neurological diseases, where multiple
                      processes such as demyelination and iron accumulation occur
                      simultaneously in the same location, a separation of
                      susceptibility sources is desirable to disentangle the
                      underlying susceptibility proportions.The basic concept of
                      separating the susceptibility effects from sources with
                      different sign within one voxel is to include information on
                      relaxation rate ΔR2∗ in the quantitative susceptibility
                      mapping reconstruction pipeline. The presented
                      reconstruction algorithm is implemented as a constrained
                      minimization problem and solved using conjugate gradients.
                      The algorithm is evaluated using a software phantom and
                      validated in MRI measurements with a phantom containing
                      mixtures of microscopic positive and negative susceptibility
                      sources. Data from three multiple sclerosis patients are
                      used to show in vivo feasibility.In numerical simulations,
                      the feasibility of disentangling susceptibility sources
                      within the same voxel was confirmed provided the critera of
                      the static dephasing regime were fulfilled. In phantom
                      experiments, the magnitude decay kernel, which is an
                      essential reconstruction parameter of the algorithm, was
                      determined to be Dm=194.5T-1s-1ppm-1, and susceptibility
                      sources could be separated in MRI measurement data.In
                      conclusion, in this study a detailed description of the
                      implementation of an algorithm for the separation of
                      positive and negative susceptibility sources within the same
                      volume element as well as its limitations is presented and
                      validated quantitatively in both simulation and phantom
                      experiments for the first time. An application to multiple
                      sclerosis lesions shows promising results for in vivo
                      usability.},
      keywords     = {Magnetic susceptibility (Other) / Microstructure (Other) /
                      Relaxation rate (Other) / Source separation (Other) / Static
                      dephasing regime (Other)},
      cin          = {E020},
      ddc          = {530},
      cid          = {I:(DE-He78)E020-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34303117},
      doi          = {10.1016/j.jmr.2021.107033},
      url          = {https://inrepo02.dkfz.de/record/169968},
}