© 2018 Wiley Periodicals, Inc. 3D imaging techniques enable the nondestructive analysis and modeling of complex structures. Among these, MRI exhibits good soft tissue contrast, but is currently less commonly used for nonclinical research than X-ray CT, even though the latter requires contrast-staining that shrinks and distorts soft tissues. When the objective is the creation of a realistic and complete 3D model of soft tissue structures, MRI data are more demanding to acquire and visualize and require extensive post-processing because they comprise noncubic voxels with dimensions that represent a trade-off between tissue contrast and image resolution. Therefore, thin soft tissue structures with complex spatial configurations are not always visible in a single MRI dataset, so that standard segmentation techniques are not sufficient for their complete visualization. By using the example of the thin and spatially complex connective tissue myosepta in lampreys, we developed a workflow protocol for the selection of the appropriate parameters for the acquisition of MRI data and for the visualization and 3D modeling of soft tissue structures. This protocol includes a novel recursive segmentation technique for supplementing missing data in one dataset with data from another dataset to produce realistic and complete 3D models. Such 3D models are needed for the modeling of dynamic processes, such as the biomechanics of fish locomotion. However, our methodology is applicable to the visualization of any thin soft tissue structures with complex spatial configurations, such as fasciae, aponeuroses, and small blood vessels and nerves, for clinical research and the further exploration of tensegrity. Anat Rec, 301:1745–1763, 2018. © 2018 Wiley Periodicals, Inc.
Publication Source (Journal or Book title)
Wood, B., Jia, G., Carmichael, O., Mcklveen, K., & Homberger, D. (2018). 3D MRI Modeling of Thin and Spatially Complex Soft Tissue Structures without Shrinkage: Lamprey Myosepta as an Example. Anatomical Record, 301 (10), 1745-1763. https://doi.org/10.1002/ar.23857