Utility of virtual monoenergetic images from spectral detector computed tomography in improving image segmentation for purposes of 3D printing and modeling.

Abstract

One of the key steps in generating three-dimensional (3D) printed models in medicine is segmentation of radiologic imaging. The software tools used for segmentation may be automated, semi-automated, or manual which rely on differences in material density, attenuation characteristics, and/or advanced software algorithms. Spectral Detector Computed Tomography (SDCT) is a form of dual energy computed tomography that works at the detector level to generate virtual monoenergetic images (VMI) at different energies/ kilo-electron volts (keV). These VMI have varying contrast and attenuation characteristics relative to material density. The purpose of this pilot project is to explore the use of VMI in segmentation for medical 3D printing in four separate clinical scenarios. Cases were retrospectively selected based on varying complexity, value of spectral data, and across multiple clinical disciplines (Vascular, Cardiology, Oncology, and Orthopedic).