The recurrent laryngeal nerve (RLN) innervates all the intrinsic muscles of the larynx that are responsible for human vocalization and language. The RLN runs along the tracheoesophageal groove bilaterally and is often accidentally damaged or transected during head and neck surgical procedures. RLN palsy and vocal cord paralysis are the most common and serious post op complications of thyroid surgeries. Patients who suffer from RLN injury can develop unilateral or bilateral vocal fold paralysis (BVFP). Theoretically, selective reinnervation of the posterior cricoarytenoid muscle would be the best treatment for BVFP. The phrenic nerve has been shown in several studies to be the best candidate to anastomose to the distal end of a severed RLN to restore glottal abduction. Successful PCA reinnervation has been sporadically achieved in both human patients and in animal models. Another notable ramification of recurrent laryngeal nerve injury is vocal instability caused by the alteration of mechanical properties within the larynx. In phonosurgery, alterations to the position and framework of the laryngeal apparatus are made to improve voice quality. Accurate and realistic synthetic models are greatly needed to predict the outcome of various adjustments to vocal cord tension and position that could be made surgically. Despite the sporadically successful attempts at PCA reinnervation, thus far, there are still several deficits in our anatomical familiarity and technological capability, which hinder the regularity of successful PCA reinnervation surgeries and our capacity to generate synthetic models of the human larynx that are both realistic and functional. We will address three of these deficits in this project using the porcine larynx as a model. Firstly, we will identify the anatomical variations of the porcine recurrent laryngeal nerve branches. A microscribe digitizer will be used to create three-dimensional mapping of the recurrent laryngeal nerve branches that are relevant to the posterior cricoarytenoid muscle and the abduction of the vocal folds. Secondly, we will develop a magnetic resonance imaging technique to correlate recurrent laryngeal nerve branching patterns with high-resolution MR images that can be used to determine the branching patterns present in a given specimen without surgery. Lastly, we will determine the distribution and composition of different tissue types found within human vocal folds. High resolution MRI, and Mallory's trichrome and H&E histological staining will be used to distinguish and identify the tissue composition of the vocal folds and surrounding laryngeal structures. Detailed information regarding vocal fold tissue composition and histological geometry will enable laryngeal modelers to select more sophisticated and life-like materials with which to construct synthetic vocal fold models.