MRI-based 3D models of cranial nerves in clinical care: a systematic review.

BACKGROUND

Technical advances in magnetic resonance imaging (MRI) acquisition and reconstruction have improved the visualization of anatomical structures such as cranial nerves (CNs) and enabled subsequent three-dimensional (3D) models for use in clinical care. However, a comprehensive overview of indications and techniques is lacking. The current study aimed to comprehensively describe and assess the techniques and applications used in MRI-based 3D modeling of CNs.

METHODS

We conducted a systematic review of articles published in Medline, Embase, and Scopus databases on clinically applied MRI-based 3D models of CNs up to December 2023. We modified the Quality Assessment Tool for Diagnostic Accuracy Studies to assess the risk of bias.

RESULTS

We analyzed 37 studies presenting virtual 3D models of CNs II, III, and V-X in proximity to pathologies in the head and neck area and intracranial, including vestibular schwannoma, skull base tumors, cerebellopontine angle tumors, and neurovascular compression syndrome. Certain studies explored alternative visualization modalities, including printed and augmented reality models. The creation of these 3D models involved the utilization of several MRI sequences and segmentation tools. The models demonstrate potential benefits for preoperative planning, intraoperative decision-making, and patient counseling.

CONCLUSION

MRI-specific sequences and segmentation techniques render CNs in 3D models, helping before and during surgery.

RELEVANCE STATEMENT: MRI-based 3D models of cranial nerves help surgeons before and during surgery and enhance patient understanding of the procedure and its risks. Wider clinical adoption requires an established workflow, technical expertise, and collaboration to ensure accessibility and knowledge sharing.

KEY POINTS: 3D modeling of cranial nerves is a promising tool for preoperative planning, surgery, and patient-doctor communication. Data heterogeneity and small sample sizes hinder definitive conclusions about the best MRI techniques and segmentation protocols for 3D visualization of cranial nerves. Adopting MRI-based 3D models widely needs a set workflow, technical skills, and team collaboration.