[Key technologies of functional fascicular groups three dimensional visualization of long segmented peripheral nerve based on two-time imaging technique and automatic registration].

Objective: To explore and solve the key technologies of the three dimensional (3D) visualization reconstruction of functional fascicular groups inside long segmented peripheral nerve.

Methods: A 20 cm ulnar nerve from upper arm of fresh adult dead body was embedded by OCT with four pieces of woman's hair which was used as locating material, then the samples were serially horizontally sliced into 400 slices with 15 microm thickness and 0.5 mm interval. All slices were stained with acetylcholinesterase (AchE) histochemical staining. After that, the 2D panorama images of the same slice were obtained with Olympus stereomicroscope and MSHOT MD90 micro figure image device before and after AchE staining. Using the layer processing technique of Photoshop image processing software, the decomposition images including complete 4 location pots were obtained, based on which the algorithm of optimized least square support vector machine (Optimized LS-SVM) and space transformation method was used to fulfill automatic registration. Finally, with artificial assistant outline obtaining, the 3D visualization reconstruction model of functional fascicular groups of 20 cm ulnar nerve was made using Amira 4.1, and the effects of reverse reduction and the suitability of 3D reconstruction software were evaluated.

Results: The two-time imaging technique based on the layer process of Photoshop image processing software had the advantages: the image outline had high goodness of fit; the locating pots of merging image was accurate; and the whole procedure was simple and fast. The algorithm of Optimized LS-SVM had high degree of accuracy, and the error rate was only 8.250%. The 3D reconstruction could display the changes of the chiastopic fusion of different nerve functional fascicular groups directly. It could extract alone, merge and combine arbitrarily, and revolve at any angles. Furthermore, the reverse reduction on arbitrarily level dissection of the 3D model was very accurately.

Conclusion: Based on the two-time imaging technique and computer image layer processing technology, the compute algorithm of auto-registration can be developed and applied to 3D visualization reconstruction of long segmented peripheral nerve. The technological processes is fast, and the reconstruction effect is good.