# Modeling Postoperative Nerve Regeneration Using Diffusion MRI: A Preclinical Study of a Novel Mathematical Approach

## TL;DR

This study uses diffusion MRI and a mathematical model to track nerve regeneration in rats, showing that it can predict recovery outcomes more effectively than current methods.

## Contribution

A novel mathematical approach based on the Gompertz function is applied to diffusion MRI for monitoring nerve regeneration.

## Key findings

- FA values at the distal section showed the strongest correlation with functional recovery (r = 0.84, p < 0.001).
- Automated FA profile analysis can help distinguish successful from unsuccessful nerve recovery.
- The method could enable early diagnosis and guide decisions for second repair surgeries.

## Abstract

Nerve regeneration after injury must occur in a timely fashion to restore function. Current methods of assessment provide limited information following trauma, resulting in delayed management and suboptimal outcomes. In this study, we evaluated the ability of diffusion magnetic resonance imaging (MRI) and a mathematical model based on the Gompertz function to monitor nerve regeneration after injury and repair.

Sprague Dawley rats were assigned to two treatment groups (sham = 2, cut, immediate repair = 7), and in vivo diffusion tensor imaging (DTI) was performed every 2 weeks until 12 weeks post‐surgery. Functional recovery was evaluated weekly over the same time period via the sciatic functional index (SFI).

After injury, SFI and DTI‐derived fractional anisotropy (FA) values exhibited similar longitudinal trends and distinctions in both sham and cut/repair (C/R) cohorts. FA values at the distal section displayed the highest correlation with behavioral indices at the region nearest to the injury (r = 0.84, p < 0.001), followed by FA values at the central section (r = 0.82, p < 0.001) and the section farthest from the injury (r = 0.70, p < 0.001).

Findings suggest that automated analyses of FA profiles along the nerve may provide insights for distinguishing successful/unsuccessful nerve recovery. This tool, once proven in a larger‐scale study, can provide clinicians with the needed tool to early diagnose nerve recovery and identify cases requiring a second repair surgery.