ABSTRACT
PURPOSE: Age-matched average 3D models facilitate both surgical planning and intraoperative guidance of cranial birth defects such as craniosynostosis. We aimed to develop an algorithm that accepts any number of CT scans as input and generates highly accurate, average models with minimal user input that are ready for 3D printing and clinical use. METHODS: Using a compiled database of 'normal' pediatric computed tomography (CT) scans, we report Normscan, an open-source platform built in Python that allows users to generate normative models of CT scans through user-defined landmarks. We use the basion, nasion, and left and right porions as anatomical landmarks for initial correspondence and then register the models using the iterative closest points algorithm before downstream averaging. RESULTS: Normscan is fast and easy to use via our user interface and also creates highly accurate average models of any number of input models. Additionally, it is highly repeatable, with coefficients of variance for the surface area and volume of the average model being less than 3% across ten independent trials. Average models can then be 3D printed and/or visualized in augmented reality. CONCLUSIONS: Normscan provides an end-to-end pipeline for the creation of average models of skulls. These models can be used for the generation of databases of specific demographic anatomical models as well as for intraoperative guidance and surgical planning. While Normscan was designed for craniosynostosis repair, due to the modular nature of the algorithm, Normscan has many applications in other areas of surgical planning and research.
ABSTRACT
Preoperative surgical planning incorporating computer-aided design and manufacturing is increasingly being utilized today within the fields of craniomaxillofacial, orthopedic, and neurosurgery. Application of these techniques for craniosynostosis reconstruction can include patient-specific anatomic reference models, "normal" reference models or patient-specific cutting/marking guides based on the presurgical plan. The major challenge remains the lack of tangible means to transfer the preoperative plan to the operating table. We propose a simple solution to utilize a digitally designed, 3D-printed "composite model" as a structural template for cranial vault reconstruction. The composite model is generated by merging the abnormal patient cranial anatomy with the "dural surface topography" of an age-matched, sex-matched, and ethnicity-matched normative skull model. We illustrate the applicability of this approach in 2 divergent cases: 22-month-old African American male with sagittal synostosis and 5-month-old White male with metopic synostosis. The aim of this technical report is to describe our application of this computer-aided design and modeling workflow for the creation of practical 3D-printed skulls that can serve as intraoperative frameworks for the correction of craniosynostosis. With success in our first 2 cases, we believe this approach of a composite model is another step in reducing our reliance on subjective guesswork, and the fundamental aspect of the workflow has a wider application within the field of craniofacial surgery for both clinical patient care and education.