Abstract
Coprolites (fossil faeces) reveal clues to ancient trophic relations, and contain inclusions representing organisms that are rarely preserved elsewhere. However, much information is lost by classical techniques of investigation, which cannot find and image the inclusions in an adequate manner. We demonstrate that propagation phase-contrast synchrotron microtomography (PPC-SRμCT) permits high-quality virtual 3D-reconstruction of coprolite inclusions, exemplified by two coprolites from the Upper Triassic locality Krasiejów, Poland; one of the coprolites contains delicate beetle remains, and the other one a partly articulated fish and fragments of bivalves.
Highlights
Many coprolites are comparable to small and underexplored Konservat-Lagerstätten in which undigested food remains, including soft tissues, preserve better than in the host rock[1]
Coprolite contents have previously almost exclusively been visualized through light microscopy of thin sections and by scanning electron microscopy (SEM)[2,3,4,5,6,7, 9, 10]
Laboratory μCT is able to image small objects at high resolutions, but typically yield unsatisfactory contrasts between mineralized tissues in fossil specimens as their contrast mechanism is based on X-ray absorption only
Summary
The coprolites were scanned using propagation phase-contrast synchrotron microtomography (PPC-SRμCT) at beamline ID19 of the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. The coprolites were scanned in half acquisition mode (i.e. the center at rotation was set at the side of the camera field of view, resulting in a doubling of the reconstructed field of view), in vertical series of 5 mm. Region growing is a tool for segmentation that selects connected voxels with similar gray scale values. The degree of difficulty in segmenting certain inclusions with this tool is related to the contrast between the inclusion of interest and the coprolite matrix as well as its connection to other inclusions (or areas with secondary mineralization) with similar gray scale values. All visible inclusions of interest were possible to isolate and segment using region growing and multiple small thresholds based on voxels with different gray scale values
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