vinkle-srivastav/TFUScapes

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tags: - medical pretty_name: tfuscapes task_categories: - other language: - en size_categories: - 1K<n<10K modalities: - npz

A Skull-Adaptive Framework for AI-Based 3D Transcranial Focused Ultrasound Simulation

Vinkle Srivastav, Juliette Puel, Jonathan Vappou, Elijah Van Houten, Paolo Cabras*, Nicolas Padoy*

*co-last authors

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Introduction

Transcranial focused ultrasound (tFUS) is an emerging modality for non-invasive brain stimulation and therapeutic intervention, offering millimeter-scale spatial precision and the ability to target deep brain structures. However, the heterogeneous and anisotropic nature of the human skull introduces significant distortions to the propagating ultrasound wavefront, which require time-consuming patient-specific planning and corrections using numerical solvers for accurate targeting. To enable data-driven approaches in this domain, we introduce TFUScapes, the first large-scale, high-resolution dataset of tFUS simulations through anatomically realistic human skulls derived from T1-weighted MRI images. We have developed a scalable simulation engine pipeline using the k-Wave pseudo-spectral solver, where each simulation returns a steady-state pressure field generated by a focused ultrasound transducer placed at realistic scalp locations. In addition to the dataset, we present DeepTFUS, a deep learning model that estimates normalized pressure fields directly from input 3D CT volumes and transducer position. The model extends a U-Net backbone with transducer-aware conditioning, incorporating Fourier-encoded position embeddings and MLP layers to create global transducer embeddings. These embeddings are fused with U-Net encoder features via feature-wise modulation, dynamic convolutions, and cross-attention mechanisms. The model is trained using a combination of spatially weighted and gradient-sensitive loss functions, enabling it to approximate high-fidelity wavefields. The TFUScapes dataset is publicly released to accelerate research at the intersection of computational acoustics, neurotechnology, and deep learning.

Please go to the repo: https://github.com/CAMMA-public/TFUScapes for more details.

Sample visualization

Citation

If you use our dataset or code in your research, please cite as:

@article{srivastav2025tfuscapes,
    author    = {Srivastav, Vinkle and Puel, Juliette and Vappou, Jonathan and Houten, Elijah Van and Cabras, Paolo and Padoy, Nicolas},
    title     = {A Skull-Adaptive Framework for AI-Based 3D Transcranial Focused Ultrasound Simulation},
    journal =   {arXiv preprint arXiv:2505.12998},
    year      = {2025},
    note     =  {\url{https://github.com/CAMMA-public/TFUScapes}},
}

Disclaimer

It is important to emphasize that the model and dataset are intended strictly for research purposes. The dataset TFUScapes is not validated for clinical decision-making and must not be used as a substitute for certified medical devices or simulation platforms. As with any simulation-based method in biomedical contexts, there remains a risk that third parties could employ these tools beyond their intended scope. To mitigate this, clear disclaimers and licensing restrictions have been implemented to underscore the non-clinical nature of the data and promote responsible use. Broader impact is expected in terms of advancing reproducibility and collaboration in scientific computing, particularly in the development of data-driven methods for neurotechnology and therapeutic ultrasound.

License

The TFUScapes dataset is publicly released under the CC-BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International). This implies that:

• Share with credit: You can copy and share the work if you give proper attribution.
• No commercial use: The material cannot be used for any commercial purposes.
• Share alike: If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

By downloading and using this dataset, you agree to these terms and conditions.