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find Author "LI Shengjie" 1 results
  • PRELIMINARY RESEARCH OF 3D RECONSTRUCTION OF SHORT-SEGMENT COMMON PERONEAL NERVEFUNCTIONAL FASCICLES

    Objective To investigate the feasibil ity of building the 3D reconstruction of short segment common peroneal nerve functional fascicles based on serial histological sections and computer technology. Methods Five cm of the common peroneal nerve in the popl iteal fossa, donated by an adult, was made into the serial transverse freezing sections(n=200) at an interval of 0.25 mm and 10 μm in thickness per section. Acetylchol inesterase staining was adopted and the nerve fascicles were observed by microscope. 2D panorama images were acquired by high-resolution digital camera under microscope (× 100) and mosaic software. Different functional fascicles were distinguished and marked on each section. The topographic database was matched by image processing software. The 3D microstructure of the fascicular groups of 5 cm common peroneal nerve was reconstructed using Amira 3.1 3D reconstruction software. Results Based on microanatomy and the results of acetylchol inesterase staining, this segmented common peroneal nerve functional fascicles was divided into sensory tract, motor tract, mixed tract and motor-predominating mixed tract. The cross merging was not evident in the nerve fascicles between deep peroneal nerve and superficial peroneal nerve, but existed within the functional fascicles of the deep peroneal nerve and the superficial peroneal nerve. The results of 3D reconstruction reflected the 3D structure of peripheral nerve and its interior functional fascicles factually, which displayed solely or in combination at arbitrary angles. Conclusion Based on serial histological sections and computer technology, the 3D microstructure of short-segment peripheral nerve functional fascicles can be reconstructed satisfactorily, indicating the feasibil ity of building 3D reconstruction of long-segmental peripheral nerve functional fascicles.

    Release date:2016-09-01 09:17 Export PDF Favorites Scan
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