001007666 001__ 1007666
001007666 005__ 20230622203101.0
001007666 0247_ $$2URN$$aurn:nbn:de:hbz:061-20230403-141602-3
001007666 0247_ $$2Handle$$a2128/34569
001007666 037__ $$aFZJ-2023-02149
001007666 041__ $$aEnglish
001007666 1001_ $$0P:(DE-Juel1)169807$$aMatuschke, Felix$$b0$$ufzj
001007666 245__ $$aNerve Fiber Modeling and 3D-PLI Simulations of a Tilting Polarization Microscope$$f2016-08-01 - 2022-07-19
001007666 260__ $$aDüsseldorf$$bUniversitäts- und Landesbibliothek Düsseldorf$$c2023
001007666 300__ $$a218
001007666 3367_ $$2DataCite$$aOutput Types/Dissertation
001007666 3367_ $$2ORCID$$aDISSERTATION
001007666 3367_ $$2BibTeX$$aPHDTHESIS
001007666 3367_ $$02$$2EndNote$$aThesis
001007666 3367_ $$0PUB:(DE-HGF)11$$2PUB:(DE-HGF)$$aDissertation / PhD Thesis$$bphd$$mphd$$s1687423557_28322
001007666 3367_ $$2DRIVER$$adoctoralThesis
001007666 502__ $$aDissertation, Heinrich Heine Universität Düsseldorf, 2023$$bDissertation$$cHeinrich Heine Universität Düsseldorf$$d2023$$o2023-02-06
001007666 520__ $$aIn 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.
001007666 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001007666 588__ $$aDataset connected to DataCite
001007666 8564_ $$uhttps://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=61984
001007666 8564_ $$uhttps://juser.fz-juelich.de/record/1007666/files/Dissertation_Felix_Matuschke.pdf$$yOpenAccess
001007666 909CO $$ooai:juser.fz-juelich.de:1007666$$popenaire$$popen_access$$purn$$pdriver$$pVDB$$pdnbdelivery
001007666 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169807$$aForschungszentrum Jülich$$b0$$kFZJ
001007666 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001007666 9141_ $$y2023
001007666 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001007666 920__ $$lyes
001007666 9201_ $$0I:(DE-Juel1)INM-1-20090406$$kINM-1$$lStrukturelle und funktionelle Organisation des Gehirns$$x0
001007666 980__ $$aphd
001007666 980__ $$aVDB
001007666 980__ $$aUNRESTRICTED
001007666 980__ $$aI:(DE-Juel1)INM-1-20090406
001007666 9801_ $$aFullTexts