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Measured and synthetic rigid head motion datasets via generative model for motion simulation and compensation in medical imaging

Goldmann M, Damm F, Hornung O, Manhart M, Preuhs A, Maier A (2026)

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Publication Type: Journal article

Publication year: 2026

Journal

Original Authors: Manuela Goldmann, Felix Damm, Oliver Hornung, Michael Manhart, Alexander Preuhs, Andreas Maier

Book Volume: 13

Issue: 06

Journal Issue: 6

DOI: 10.1117/1.JMI.13.6.062206

Abstract

Purpose

Rigid head motion during interventional C-arm cone-beam CT (CBCT) is a major source of image degradation. Learning-based motion estimation requires realistic training data, but ground-truth motion is scarce, limiting direct validation of compensation trajectories. We address this gap with an open resource consisting of tracked real motion and pregenerated synthetic motion, along with a pretrained variational autoencoder (VAE) to generate larger ground-truth datasets.

Approach

Using stereo optical tracking, we recorded rigid 6-DoF head motion trajectories from 25 volunteers lying head-first supine on an examination table, resembling a clinical setting. After data preprocessing, we trained a VAE on 120 sequences of 10 s at 30 Hz. Motion is represented in patient-centered coordinates to support transformation to arbitrary scan geometries. Similarity between measured and generated data is assessed via distributional distances, correlation metrics, low-dimensional embeddings, and a posthoc analysis of the learned latent space.

Results

Evaluated based on 120 generated sequences, the trained VAE is capable of producing diverse 6-DoF trajectories that preserve real-world data correlation structure. Distributional and frequency-domain metrics, along with t-SNE embeddings, show overlap between real and synthetic samples without evidence of mode collapse or training data replication.

Conclusions

This work provides an openly released resource comprising measured trajectories, a synthetic dataset, and pretrained VAE weights together with full training and evaluation code, combining rigid 6-DoF head motion measured in a realistic C-arm setting with a retrainable generative model. It is intended to support reproducible development, benchmarking, and comparison of head motion estimation methods in medical imaging modalities.

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How to cite

APA:

Goldmann, M., Damm, F., Hornung, O., Manhart, M., Preuhs, A., & Maier, A. (2026). Measured and synthetic rigid head motion datasets via generative model for motion simulation and compensation in medical imaging. Journal of Medical Imaging, 13(6). https://doi.org/10.1117/1.JMI.13.6.062206

MLA:

Goldmann, Manuela, et al. "Measured and synthetic rigid head motion datasets via generative model for motion simulation and compensation in medical imaging." Journal of Medical Imaging 13.6 (2026).

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