Abstract: DRACO: Differentiable Reconstruction for Arbitrary CBCT Orbits

Ye C, Schneider LS, Sun Y, Thies M, Mei S, Maier A (2026)

Publication Type: Conference contribution

Publication year: 2026

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Pages Range: 334-334

Conference Proceedings Title: Informatik aktuell

Event location: Lübeck

ISBN: 9783658510992

DOI: 10.1007/978-3-658-51100-5_65

Abstract

Objective: this study introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits [1], addressing the computational and memory challenges associated with traditional iterative reconstruction algorithms. Approach: the proposed method employs a differentiable shift-variant filtered backprojection neural network, optimized for arbitrary trajectories. By integrating known operators into the learning model, the approach minimizes the number of trainable parameters while enhancing model interpretability. This framework adapts seamlessly to specific orbit geometries, including non-continuous trajectories such as circular-plus-arc or sinusoidal paths, enabling faster and more accurate CBCT reconstructions. Main results: experimental validation demonstrates that the method significantly accelerates reconstruction, reducing computation time by over 97% compared to conventional iterative algorithms. It achieves superior or comparable image quality with reduced noise, as evidenced by a 38.6% reduction in mean squared error, a 7.7% increase in peak signal-to-noise ratio, and a 5.0% improvement in the structural similarity index measure. The flexibility and robustness of the approach are confirmed through its ability to handle data from diverse scan geometries. Significance: this method represents a significant advancement in interventional medical imaging, particularly for robotic C-arm CT systems, enabling real-time, high-quality CBCT reconstructions for customized orbits. It offers a transformative solution for clinical applications requiring computational efficiency and precision in imaging. Code availability: code is available at https://github.com/ChengzeYe/Defrise-and-Clack-reconstruction.

Authors with CRIS profile

How to cite

APA:

Ye, C., Schneider, L.-S., Sun, Y., Thies, M., Mei, S., & Maier, A. (2026). Abstract: DRACO: Differentiable Reconstruction for Arbitrary CBCT Orbits. In Heinz Handels, Katharina Breininger, Thomas Deserno, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 334-334). Lübeck, DE: Springer Science and Business Media Deutschland GmbH.

MLA:

Ye, Chengze, et al. "Abstract: DRACO: Differentiable Reconstruction for Arbitrary CBCT Orbits." Proceedings of the Bildverarbeitung für die Medizin Workshop, BVM 2026, Lübeck Ed. Heinz Handels, Katharina Breininger, Thomas Deserno, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2026. 334-334.

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