Development of a Full Body CAD Dataset for Computational Modeling: A Multi-modality Approach

Abstract

The objective of this study was to develop full body CAD geometry of a seated 50th percentile male. Model development was based on medical image data acquired for this study, in conjunction with extensive data from the open literature. An individual (height, 174.9 cm, weight, 78.6 ± 0.77 kg, and age 26 years) was enrolled in the study for a period of 4 months. 72 scans across three imaging modalities (CT, MRI, and upright MRI) were collected. The whole-body dataset contains 15,622 images. Over 300 individual components representing human anatomy were generated through segmentation. While the enrolled individual served as a template, segmented data were verified against, or augmented with, data from over 75 literature sources on the average morphology of the human body. Non-Uniform Rational B-Spline (NURBS) surfaces with tangential (G1) continuity were constructed over all the segmented data. The sagittally symmetric model consists of 418 individual components representing bones, muscles, organs, blood vessels, ligaments, tendons, cartilaginous structures, and skin. Length, surface area, and volumes of components germane to crash injury prediction are presented. The total volume (75.7 × 103 cm(3)) and surface area (1.86 × 102 cm(2)) of the model closely agree with the literature data. The geometry is intended for subsequent use in nonlinear dynamics solvers, and serves as the foundation of a global effort to develop the next-generation computational human body model for injury prediction and prevention.