Learned Shift-variant CBCT Reconstruction Weights for Non-continuous Trajectories

Ye C, Schneider LS, Sun Y, Thies M, Maier A (2025)

Publication Type: Conference contribution

Publication year: 2025

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Pages Range: 292-297

Conference Proceedings Title: Informatik aktuell

Event location: Regensburg, DEU

ISBN: 9783658474218

DOI: 10.1007/978-3-658-47422-5_68

Abstract

The differentiable shift-variant filtered back-projection neural network provides an efficient solution for the reconstruction of cone-beam computed tomography (CBCT) data with arbitrary source trajectories. This data-driven approach enables the automatic estimation of trajectory-specific redundancyweights, which are difficult to calculate in practice. The objective of this study is to learn redundancy weights tailored for non-continuous trajectories. The experimental results using random and random nearest-neighbor reordered (RNNR) trajectories show that the model achieves effective image reconstruction, even with non-continuous trajectories. A quantitative analysis of the reconstruction results demonstrates minimal variation in performance metrics across different random seeds, which highlights the model’s robustness and stability. Furthermore, the model’s consistency across random and RNNR trajectories demonstrates that a data-driven approach to learning redundancy weights is not strictly dependent on trajectory ordering. This marks a significant improvement over traditional analytic method. The approach provides a flexible and effective alternative for CBCT imaging in scenarios with non-continuous or even unordered trajectories, opening new possibilities for imaging applications.

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How to cite

APA:

Ye, C., Schneider, L.-S., Sun, Y., Thies, M., & Maier, A. (2025). Learned Shift-variant CBCT Reconstruction Weights for Non-continuous Trajectories. In Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff (Eds.), Informatik aktuell (pp. 292-297). Regensburg, DEU: Springer Science and Business Media Deutschland GmbH.

MLA:

Ye, Chengze, et al. "Learned Shift-variant CBCT Reconstruction Weights for Non-continuous Trajectories." Proceedings of the German Conference on Medical Image Computing, 2025, Regensburg, DEU Ed. Christoph Palm, Katharina Breininger, Thomas Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Thomas M. Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2025. 292-297.

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