Conditional Latent Diffusion Model with Fourier-based Motion Modelling for Virtual Population Synthesis
arXiv:2606.03827v1 Announce Type: cross Abstract: In-silico trials of medical devices require the generation of virtual populations of anatomies. In cardiovascular applications, virtual anatomy is typically represented as a 3D+t mesh sampled from a generative model. However, most existing mesh generators focus on static anatomy, while sequence models often lack explicit periodicity. To this end, we propose 4D F-MeshLDM, a conditional generative framework comprising a convolutional mesh VAE to encode meshes, a structural latent space that parameterises motion using a truncated Fourier series, a
Advances in generative AI and medical imaging are converging, making the computational generation of complex anatomical models increasingly feasible.
This development is crucial for pharmaceutical research, medical device development, and personalized medicine, reducing the need for costly and time-consuming in-vivo trials.
The ability to synthesize dynamic 3D+t anatomical models will accelerate 'in-silico' clinical trials, enabling faster iteration and validation of medical innovations.
- · Medical Device Manufacturers
- · Pharmaceutical Companies
- · Healthcare AI Developers
- · Computational Biologists
- · Traditional Clinical Research Organizations (CROs)
- · Companies reliant solely on physical prototyping
Reduced development timelines and costs for new medical technologies.
Increased accuracy and personalization of medical treatments and devices based on tailored virtual patient models.
Ethical and regulatory frameworks will need to adapt to validate and oversee treatments developed primarily through virtual trials.
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Read at arXiv cs.AI