Generative tissue modeling for customized biomechanical analysis: a data-driven synthesis framework of...

Conventional computational modeling of human body and tissue relies on time-consuming reconstruction from medical images. Instead, generative artificial intelligence synthesizes novel samples with few marginal costs. However, existing models are incompatible with 3D data structures. This study aims to establish a 3D generative tissue modeling pipeline. The key objective is to address the following technical bottlenecks: 1) tackle the neural-network incompatibility of discretization-agnostic meshes, 2) formulate a sequence-type architecture for shape prior learning and generation, and 3) realize smooth source-to-target mapping.

We propose a data-driven non-rigid registration that learns geometry-informed features through self-supervised pretraining, and infers high-fidelity correspondences accordingly. A template mesh is uniformly registered onto 90 femur shapes to align inter-vertex connectivity. A variational autoencoder (VAE) is trained on the structurally aligned samples, and synthesizes novel shapes thereafter. Left femur of a baseline finite-element model is mapped onto the synthesized instances respectively, and the femurs are computationally loaded to investigate biomechanics with diverse morphologies.

Tuning the six latent dimensions of the trained VAE independently generates shapes in heterogenous morphological patterns, i.e., overall size, overall bending curvature, slenderness, shaft length, fine-level shaft curvature, and local epiphysis/metaphysis style. Quantitatively, varying the first latent results in an 83.4 mm change in femur length, while the second latent controls the equivalent radius of shaft segment in a range from 66.8 mm to 862.6 mm. The VAE model synthesizes geometrically valid shapes within up to 3 standard deviations (>99.7%) of the entire latent. 10 new femurs are synthesized and registered onto a baseline finite-element model in less than 100 s for each case. Preliminary analysis with three-point bending load reveals morphological variation might have a significant influence on deformation pattern and bending biomechanics.

This study establishes a contemporary generative paradigm for tissue modeling, and demonstrates efficacy and feasibility of biomechanics investigation with synthetic shapes. Our method produces high-fidelity, simulation-ready models in only minutes. The pretraining scheme is scalable to multiple anatomical structures and sheds light to foundation model of 3D anatomies, which might promisingly benefit a lot of production workflows, e.g., active-passive vehicle safety, robot-assisted surgery, and all biomechanics/morphology-relevant tasks.