% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Menzel:907203,
      author       = {Menzel, Miriam and Reuter, Jan A. and Gräßel, David and
                      Costantini, Irene and Amunts, Katrin and Axer, Markus},
      title        = {{A}utomated computation of nerve fibre inclinations from
                      3{D} polarised light imaging measurements of brain tissue},
      journal      = {Scientific reports},
      volume       = {12},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2022-01891},
      pages        = {4328},
      year         = {2022},
      abstract     = {The method 3D polarised light imaging (3D-PLI) measures the
                      birefringence of histological brain sections to determine
                      the spatial course of nerve fibres (myelinated axons). While
                      the in-plane fibre directions can be determined with high
                      accuracy, the computation of the out-of-plane fibre
                      inclinations is more challenging because they are derived
                      from the amplitude of the birefringence signals, which
                      depends e.g. on the amount of nerve fibres. One possibility
                      to improve the accuracy is to consider the average
                      transmitted light intensity (transmittance weighting). The
                      current procedure requires effortful manual adjustment of
                      parameters and anatomical knowledge. Here, we introduce an
                      automated, optimised computation of the fibre inclinations,
                      allowing for a much faster, reproducible determination of
                      fibre orientations in 3D-PLI. Depending on the degree of
                      myelination, the algorithm uses different models
                      (transmittance-weighted, unweighted, or a linear
                      combination), allowing to account for regionally specific
                      behaviour. As the algorithm is parallelised and GPU
                      optimised, it can be applied to large data sets. Moreover,
                      it only uses images from standard 3D-PLI measurements
                      without tilting, and can therefore be applied to existing
                      data sets from previous measurements. The functionality is
                      demonstrated on unstained coronal and sagittal histological
                      sections of vervet monkey and rat brains.},
      cin          = {INM-1},
      ddc          = {600},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / JL SMHB - Joint Lab Supercomputing and Modeling
                      for the Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(EU-Grant)945539 / G:(DE-Juel1)JL
                      SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000769065000026},
      doi          = {10.1038/s41598-022-08140-0},
      url          = {https://juser.fz-juelich.de/record/907203},
}