The Inner Complexities of Multimodal Medical Data: Bitmap-Based 3D Printing for Surgical Planning Using Dynamic Physiology

Abstract

Medical 3D printing has historically been limited to representations of hard structures such as bone, using computed tomography (CT) data. Most often, these models are fabricated using a single, rigid material that provides anatomical accuracy but lacks the spatial material heterogeneity needed to represent soft tissue mechanical properties. In this work, we present a process for modeling the soft tissue mechanical properties captured in magnetic resonance imaging data. Our approach uses voxel-based 3D printing and novel material formulations to generate models with spatially distributed material properties that replicate the mechanical behavior of human tissue. The workflow leverages established medical imaging techniques to create material representations spanning the range of human tissue stiffness. We demonstrate a pediatric application with fabrication of a patient-specific aortic arch model possessing realistic material properties derived from magnetic resonance elastography data. This model accurately reproduced the dynamic properties of the specific patient’s cardiovascular physiology, allowing the surgical team to replicate the operative intervention on the model and optimize their surgical approach. The results of this work demonstrate a practical method for fabricating models with spatially distributed material properties from clinical data, a significant step toward true-to-life medical models for patient-specific surgical planning and medical device design.

BibTeX

@article{jacobson2023inner,
  title = {The Inner Complexities of Multimodal Medical Data: Bitmap-Based 3D Printing for Surgical Planning Using Dynamic Physiology},
  author = {Jacobson, Nicholas M and Brusilovsky, Jane and Ducey, Robert and Stence, Nicholas V and Barker, Alex J and Mitchell, Max B and Smith, Lawrence and MacCurdy, Robert and Weaver, James C},
  journal = {3D Printing and Additive Manufacturing},
  volume = {10},
  number = {5},
  pages = {855--868},
  year = {2023},
  publisher = {Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…},
  doi = {10.1089/3dp.2022.0304},
}