A computational framework for evaluating outcomes in infant craniosynostosis reconstruction

Abstract

Historically, surgical outcomes in craniosynostosis have been evaluated by qualitative analysis, direct and indirect anthropometry, cephalometrics, and CT craniometric analysis.

Three-dimensional meshes constructed from 3dMD images acquired on patients with synostosis at multiple times across the course of surgical treatment provide ideal raw data for a novel approach to 3D geometric shape analysis of surgical results.

We design a automatic computational framework for evaluating and visualizing the results of infant cranial surgeries based on 3dMD images. The goal of this framework is to assist surgeons in evaluating the efficacy of their surgical techniques. Feedback from surgeons in Texas Children's Hospital confirms that this framework is a robust computational system within which surgical outcomes in synostosis can be accurately and meaningfully evaluated.

We also propose an algorithm to generate normative infant cranial models from the input of 3D meshes, which are extracted from CT scans of normal infant skulls. Comparing of the head shape of an affected subject with a normal control will more clearly illustrate in what aspect the subject's head deviates from the norm. Comparing of a post-treatment subject's head shape and an age-matched control would allow assessing of a specific treatment approach or surgical technique.