Guiding Device for Precision Grafting of Peripheral Nerves in Complete Thoracic Spinal Cord Injury: Design and Sizing for Clinical Trial.

Background: In an effort to translate preclinical success in achieving spinal cord regeneration through peripheral nerve grafts, this study details the design and sizing of a guiding device for precision grafting of peripheral nerves for use in a clinical trial in complete (AIS-A) thoracic spinal cord injury (SCI). The device's design and sizing are compared to a simulation of human spinal cord sizes based on the best available data.

Methods: Spinal cord segmental sizes were generated by computer simulation based on data from a meta-analysis recently published by our group. Thoracic segments T2-T12 were plotted, and seven elliptical shapes were positioned across the center of the distribution of sizes. Geometrical measures of error-of-fit were calculated. CAD modeling was used to create cranial and caudal interfaces for the human spinal cord, aiming to guide descending white matter tracts to gray matter at the caudal end of the device and ascending white matter tracts to gray matter at the cranial end of the device. The interfaces were compared qualitatively to the simulated spinal cord sizes and gray-to-white matter delineations.

Results: The mean error-of-fit comparing simulated spinal cord segments T2-T12 to the best elliptical shape was 0.41 and 0.36 mm, and the 95th percentile was found at 1.3 and 0.98 mm for transverse and anteroposterior diameter, respectively. A guiding device design was reached for capturing the majority of corticospinal axons at the cranial end of the device and guiding them obliquely to gray matter at the caudal end of the device. Based on qualitative comparison, the vast majority of spinal cord sizes generated indicate an excellent fit to the device's interfaces.

Conclusion: A set of SCI guiding devices of seven sizes can cover the variability of human thoracic spinal cord segments T2-T12 with an acceptable error-of-fit for the elliptical shape as well as guiding channels. The computational framework developed can be used with other medical technologies involving the human spinal cord where exact sizes and positioning are of importance.