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000861890 0247_ $$2doi$$a10.1007/978-3-658-25326-4_17
000861890 037__ $$aFZJ-2019-02311
000861890 041__ $$aEnglish
000861890 1001_ $$0P:(DE-Juel1)167496$$aNolden, Marius$$b0$$eCorresponding author$$ufzj
000861890 1112_ $$aBildverarbeitung für die Medizin$$cLübeck$$d2019-03-17 - 2019-03-19$$wGermany
000861890 245__ $$aTracing of Nerve Fibers Through Brain Regions of Fiber Crossings in Reconstructed 3D-PLI Volumes
000861890 260__ $$aBerlin, Heidelberg$$bSpringer$$c2019
000861890 29510 $$aBildverarbeitung für die Medizin 2019
000861890 300__ $$a62 - 67
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000861890 520__ $$aThree-dimensional (3D) polarized light imaging (PLI) is able to reveal nerve fibers in the human brain at microscopic resolution. While most nerve fiber structures can be accurately visualized with 3D-PLI, the currently used physical model (based on Jones Calculus) is not well suited to distinguish steep fibers from specific fiber crossings. Hence, streamline tractography algorithms tracing fiber pathways get easily misdirected in such brain regions. For the presented study, we implemented and applied two methods to bridge areas of fiber crossings: (i) extrapolation of fiber points with cubic splines and (ii) following the most frequently occurring orientations in a defined neighborhood based on orientation distribution functions gained from 3D-PLI measurements (pliODFs). Applied to fiber crossings within a human hemisphere, reconstructed from 3D-PLI measurements at 64 microns in-pane resolution, both methods were demonstrated to sustain their initial tract direction throughout the crossing region. In comparison, the ODF-method offered a more reliable bridging of the crossings with less gaps.
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000861890 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
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000861890 7001_ $$0P:(DE-Juel1)159224$$aSchubert, Nicole$$b1$$ufzj
000861890 7001_ $$0P:(DE-Juel1)164129$$aSchmitz, Daniel$$b2$$ufzj
000861890 7001_ $$0P:(DE-Juel1)151332$$aMüller, Andreas$$b3$$ufzj
000861890 7001_ $$0P:(DE-Juel1)131632$$aAxer, Markus$$b4$$ufzj
000861890 773__ $$a10.1007/978-3-658-25326-4_17$$p62-67
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000861890 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x1
000861890 9141_ $$y2019
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