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

Area of Science:

• Computational Biology
• Artificial Intelligence
• Biomedical Engineering

Background:

• Conventional computational modeling of human tissues from medical images is time-consuming.
• Existing generative AI models are not compatible with 3D data structures.
• A need exists for efficient, 3D-compatible generative models for tissue synthesis.

Purpose of the Study:

• To establish a 3D generative tissue modeling pipeline using artificial intelligence.
• To address technical challenges including neural network incompatibility with meshes, shape prior learning, and smooth mapping.
• To enable rapid synthesis of high-fidelity, simulation-ready anatomical models.

Main Methods:

• Developed a data-driven, non-rigid registration method with self-supervised pretraining for geometry-informed features.
• Utilized a variational autoencoder (VAE) trained on 90 aligned femur shapes to synthesize novel morphologies.
• Mapped finite-element models onto synthesized shapes for biomechanical analysis under load.

Main Results:

• Independent tuning of VAE latent dimensions controlled specific morphological features (size, curvature, slenderness, etc.).
• VAE synthesized geometrically valid femur shapes, with variations in length up to 83.4 mm and radius from 66.8 to 862.6 mm.
• Generated 10 new femurs and registered them to a baseline model in under 100 seconds each; preliminary analysis indicated morphology significantly influences biomechanics.

Conclusions:

• Established a novel generative paradigm for efficient 3D tissue modeling and biomechanical investigation.
• The method produces high-fidelity, simulation-ready models in minutes, demonstrating scalability to multiple anatomical structures.
• This foundation model for 3D anatomies has potential applications in vehicle safety, robotic surgery, and morphology-relevant research.