Soft-tissue Driven Craniomaxillofacial Surgical Planning

TL;DR

This paper introduces a soft-tissue driven framework for craniomaxillofacial surgical planning that improves accuracy by directly modeling facial soft-tissue responses to bony movements, overcoming limitations of traditional bone-driven methods.

Contribution

The novel framework combines a bony planner and facial simulator networks to automatically generate and verify surgical plans based on soft-tissue outcomes.

Findings

Significantly improves planning accuracy over traditional methods

Reduces iterative revisions in surgical planning

Validated on clinical dataset with positive results

Abstract

In CMF surgery, the planning of bony movement to achieve a desired facial outcome is a challenging task. Current bone driven approaches focus on normalizing the bone with the expectation that the facial appearance will be corrected accordingly. However, due to the complex non-linear relationship between bony structure and facial soft-tissue, such bone-driven methods are insufficient to correct facial deformities. Despite efforts to simulate facial changes resulting from bony movement, surgical planning still relies on iterative revisions and educated guesses. To address these issues, we propose a soft-tissue driven framework that can automatically create and verify surgical plans. Our framework consists of a bony planner network that estimates the bony movements required to achieve the desired facial outcome and a facial simulator network that can simulate the possible facial changes resulting from the estimated bony movement plans. By combining these two models, we can verify and determine the final bony movement required for planning. The proposed framework was evaluated using a clinical dataset, and our experimental results demonstrate that the soft-tissue driven approach greatly improves the accuracy and efficacy of surgical planning when compared to the conventional bone-driven approach.