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Spatial correlation of 2D hard-tissue histology with 3D microCT scans through 3D printed phantoms

Hard-tissue histology—the analysis of thin two-dimensional (2D) sections—is hampered by the opaque nature of most biological specimens, especially bone. Therefore, the cutting process cannot be assigned to regions of interest. In addition, the applied cutting-grinding method is characterized by sign...

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Detalles Bibliográficos
Autores principales: Nolte, Philipp, Brettmacher, Marcel, Gröger, Chris Johann, Gellhaus, Tim, Svetlove, Angelika, Schilling, Arndt F., Alves, Frauke, Rußmann, Christoph, Dullin, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613209/
https://www.ncbi.nlm.nih.gov/pubmed/37898676
http://dx.doi.org/10.1038/s41598-023-45518-0
Descripción
Sumario:Hard-tissue histology—the analysis of thin two-dimensional (2D) sections—is hampered by the opaque nature of most biological specimens, especially bone. Therefore, the cutting process cannot be assigned to regions of interest. In addition, the applied cutting-grinding method is characterized by significant material loss. As a result, relevant structures might be missed or destroyed, and 3D features can hardly be evaluated. Here, we present a novel workflow, based on conventual microCT scans of the specimen prior to the cutting process, to be used for the analysis of 3D structural features and for directing the sectioning process to the regions of interest. 3D printed fiducial markers, embedded together with the specimen in resin, are utilized to retrospectively register the obtained 2D histological images into the 3D anatomical context. This not only allows to identify the cutting position, but also enables the co-registration of the cell and extracellular matrix morphological analysis to local 3D information obtained from the microCT data. We have successfully applied our new approach to assess hard-tissue specimens of different species. After matching the predicted microCT cut plane with the histology image, we validated a high accuracy of the registration process by computing quality measures namely Jaccard and Dice similarity coefficients achieving an average score of 0.90 ± 0.04 and 0.95 ± 0.02, respectively. Thus, we believe that the novel, easy to implement correlative imaging approach holds great potential for improving the reliability and diagnostic power of classical hard-tissue histology.