Evaluation of select biocompatible markers for labelling peripheral nerves on 11.7 T MRI.

Highlights • We assessed the efficacy of implantable markers for labeling peripheral nerves. • Semi-cylindrical silicone adhered to the epineurium yielded the best results. • Silicone produced a reliable T2-signal without impacting DTI tractography. Abstract Background Peripheral nerve injury is often followed by a highly variable recovery process with respect to both rapidity and efficacy. Identifying post-nerve injury phenomena is key to assessing the merit and timing of surgery as well as to tracking nerve recovery postoperatively. Diffusion Tensor Imaging (DTI) has been investigated in the clinical and research settings as a noninvasive technique to both assess and monitor each patient's unique case of peripheral nerve damage. New method We identify a MRI-suitable marker for tracking the exact site of either nerve injury or coaptation following surgical repair to aid with DTI analysis. Results Due to artefact and disruption of tractography, silver wire and microvascular clips were not suitable markers. AxoGuard®, 4-0 vicryl suture, and 10-0 polyamide suture, although detectable, did not produce a signal easily distinguished from post-surgical changes. Silicone was easily identifiable and stable in both the acute and delayed time points, exhibited negligible impact on DTI parameters, and possessed geometry to prevent nerve strangulation. Comparison with existing method Prior studies have not assessed the efficacy of other markers nor have they assessed silicone for potential artefact with DTI parameter analysis. Furthermore, this work demonstrates the reliability and compatibility of silicone in the delayed postoperative time period and includes its unique imaging appearance on high-resolution 11.7 MRI. Conclusion Semi-cylindrical silicone tubing can be used as a safe, reliable, and readily available radiological marker to visualize and monitor a region of interest on a rodent's peripheral nerve for aiding assessments with diffusion tensor imaging. [ABSTRACT FROM AUTHOR]