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@PHDTHESIS{Matuschke:1007666,
      author       = {Matuschke, Felix},
      title        = {{N}erve {F}iber {M}odeling and 3{D}-{PLI} {S}imulations of
                      a {T}ilting {P}olarization {M}icroscope},
      school       = {Heinrich Heine Universität Düsseldorf},
      type         = {Dissertation},
      address      = {Düsseldorf},
      publisher    = {Universitäts- und Landesbibliothek Düsseldorf},
      reportid     = {FZJ-2023-02149},
      pages        = {218},
      year         = {2023},
      note         = {Dissertation, Heinrich Heine Universität Düsseldorf,
                      2023},
      abstract     = {In the Fiber Architecture group of the Institute of
                      Neuroscience and Medicine, Structuraland Functional
                      Organization of the Brain (INM-1), 3D Polarized Light
                      Imaging (3D-PLI)microscopy is used to measure the
                      orientation of nerve fibers in unstained brain
                      sections.Interpretation of the measurement can be
                      challenging for certain regions, for examplewhere fibers
                      cross or are oriented perpendicular to the sectioning plane.
                      To understandthe behavior of the measured signal of such
                      structures without further external influences,such as
                      non-ideal optics, simulations are used where each parameter
                      is known. In orderto perform simulations, virtual tissue
                      models are needed and a virtual 3D-PLI microscope,being
                      capable of simulating the influence of the tissue on the
                      light.In order to design realistic models of dense nerve
                      fiber tissue, it must be ensured thatindividual nerve fibers
                      do not overlap. This is especially difficult to design in
                      advancefor interwoven structures, as is occurs in nerve
                      fiber crossings. Therefore, a nerve fibermodeling
                      specialized algorithm was designed in this thesis. The
                      algorithm will checka given volume for overlaps of single
                      nerve fibers, and then slowly push them apart atthe affected
                      locations. Thus, a collision-free tissue model is created
                      over time. Thepre-existing simulation algorithm of the 3D
                      PLI microscope was completely redesigned aspart of this
                      work. The algorithm is now able to run in parallel on
                      multiple CPU cores aswell as computational clusters. Thus, a
                      large number of simulations can be performed,allowing for
                      greater statistics in the analyses. These two algorithms
                      were published inthe software package fiber architecture
                      simulation toolbox of 3D-PLI (fastPLI).Finally, in this
                      thesis, nerve fiber models consisting of two nerve fiber
                      populations,i. e. two densely packed crossing nerve fiber
                      bundles, were created and subsequentlysimulated. The results
                      show, that the orientation of the nerve fiber population,
                      whichhas a higher proportion in the volume, can be
                      determined. With the current resolution ofthe microscopes
                      used, it is not possible to determine both fiber population
                      orientationsindividual. The measured orientation seems to
                      follow the circular mean as a functionon the proportional
                      volume fraction of the nerve fiber populations, taking into
                      accountthe decrease of the measured signal due to the
                      increasing tilt angle. In summary, thedevelopment of the
                      algorithm for modeling nerve fibers together with the
                      simulation ina toolbox has proven to be a suitable tool to
                      be able to investigate questions quicklythrough
                      simulations.},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:hbz:061-20230403-141602-3},
      url          = {https://juser.fz-juelich.de/record/1007666},
}