A Multi-Modality Image Data Collection Protocol for Full Body Finite Element Model Development

The geometry of these models will be based off the anatomy of four individuals meeting extensive prescreening requirements and representing the 5th and 50th percentile female, and the 50th and 95th percentile male. Target values for anthropometry are determined by literature sources. Because of the relative strengths of various modalities commonly in use today in the clinical and engineering worlds, a multi-modality approach is outlined. This approach involves the use of Computed Tomography (CT), upright and closed-bore Magnetic Resonance Imaging (MRI), and external anthropometric measurements. CT data provide sub-millimeter resolution and slice thickness of the subjects in the supine and an approximately seated position. Closed-bore MRI complements CT data by providing high-resolution images with improved contrast between soft tissues. MRI pulse sequences that image fat-water interfaces out of phase are used to enhance contrast and facilitate segmenting organ and muscle boundaries.

Upright MRI data complement closed-bore data by enabling quantification of morphological changes that occur when a subject is oriented upright with respect to gravity. The final component in this suite of image data is a set of external anthropometry (EA) measurements. EA measurements include three-dimensional point cloud acquisition of external bony landmarks as well as surface contours. These data serve as a valuable geometric validation tool for the assembled full-body FEA models.

Protocol development results, including preliminary image data sets, in-plane resolution and slice thickness achieved for each modality, pulse sequence designs for MRI acquisition protocols, and custom positioning systems used in image acquisition are presented. The approach outlined in this study is expected to provide sufficient data to develop models in both the seated and standing posture. This suite of imaging and anthropometry data will serve as a strong foundation for the collaborative development of a group of full-body FEA models for injury prediction in the coming years.