Surface-guided breathing signal integration in breathing-adapted intelligent 4D computed tomography: prototype implementation and comparison with an infrared marker-based system

Lackner N, Moser T, Young J, Dickmann J, That V, Hofmann C, Karius A, Ahmad M, Ott O, Putz F, Fietkau R, Bert C, Szkitsak J (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Physics and Imaging in Radiation Oncology Elsevier

Book Volume: 36

Article Number: 100848

DOI: 10.1016/j.phro.2025.100848

Abstract

Background and purpose: Breathing-adapted intelligent four-dimensional computed tomography (i4DCT) reduces motion artifacts during irregular breathing using real-time surrogate signal analysis to control X-ray timing. Current implementations rely on infrared (IR) markers or pressure belts. Surface-guided radiation therapy (SGRT), a widely used markerless technique in radiotherapy, has not been evaluated for direct i4DCT integration. This study tested SGRT for i4DCT acquisition in a phantom setup and benchmarked it against an infrared marker-based system. Materials and methods: Phantom measurements with clinically relevant regular and irregular breathing signals were performed on a prototype CT scanner with direct SGRT control. To improve SGRT-based surrogate accuracy, a table motion correction profile was empirically modeled, and a vendor-specific prediction algorithm was implemented. Surrogate signal accuracy, latency, and motion reconstruction accuracy were compared between SGRT and an established IR system using amplitude- and phase-based reconstructions. Results: SGRT exhibited an absolute latency of ∼ 63 ms, compared to ∼ 23 ms for the IR system. Across regular and irregular breathing signals, SGRT breathing signals showed Root Mean Square Error (RMSE) values up to ∼ 1.5 mm, while correlation with the ideal signal remained high (r = 1). Tumor center-of-mass deviations in amplitude-based reconstructions reached 1.5 mm without prediction at 20 breaths-per-minute and 15 mm amplitude, but reduced to < 0.5 mm with 50 ms prediction. Both amplitude- and phase-based reconstructions showed improved agreement with ideal motion when prediction was applied, with phase-based reconstructions yielding better accuracy. Conclusions: These findings support SGRT as a clinically viable non-contact alternative to IR tracking in i4DCT, especially when combined with motion correction and predictive modeling.

Authors with CRIS profile

Niklas Lackner Department of Radiation Oncology Andre Karius Department of Radiation Oncology Mushawar Ahmad Department of Radiation Oncology Oliver Ott Medizinische Fakultät Florian Putz Department of Radiation Oncology Rainer Fietkau Lehrstuhl für Strahlentherapie Christoph Bert Professur für Med. Strahlenphysik Juliane Szkitsak Department of Radiation Oncology

Involved external institutions

Vision RT Ltd. GB United Kingdom (GB) Siemens Healthineers

How to cite

APA:

Lackner, N., Moser, T., Young, J., Dickmann, J., That, V., Hofmann, C.,... Szkitsak, J. (2025). Surface-guided breathing signal integration in breathing-adapted intelligent 4D computed tomography: prototype implementation and comparison with an infrared marker-based system. Physics and Imaging in Radiation Oncology, 36. https://doi.org/10.1016/j.phro.2025.100848

MLA:

Lackner, Niklas, et al. "Surface-guided breathing signal integration in breathing-adapted intelligent 4D computed tomography: prototype implementation and comparison with an infrared marker-based system." Physics and Imaging in Radiation Oncology 36 (2025).

BibTeX: Download