Neural Network-based Sinogram Upsampling in Real-measured CT Reconstruction

Augustin L, Wagner F, Thies M, Maier A (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: Springer Vieweg

Series: Informatik aktuell

City/Town: Wiesbaden

Pages Range: 310-315

Conference Proceedings Title: Bildverarbeitung für die Medizin 2024. BVM 2024

Event location: Erlangen DE

ISBN: 9783658440367

DOI: 10.1007/978-3-658-44037-4_80

Abstract

Computed tomography (CT) is one of the most popular non-invasive medical imaging modalities. A major downside of medical CT is the exposure of the patient to high-energy X-rays during image acquisition. One way to reduce the amount of ionising radiation is to record fewer projective views and then upsample the resulting subsampled sinogram. Post acquisition, this can be achieved through conventional sinogram interpolation algorithms or using neural networks. This paper compares the results of two upsampling network architectures with the results of conventional sinogram interpolation. We found that for subsampling factors two and four, the neural networks did not substantially improve the model predictions in terms of structured similarity and peak signal-to-noise ratio compared to conventional sinogram interpolation. This suggests that, for these subsampling factors and the given dataset, interpolation approximates the problemwell enough.

Authors with CRIS profile

Fabian Wagner Lehrstuhl für Informatik 5 (Mustererkennung) Mareike Thies Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

How to cite

APA:

Augustin, L., Wagner, F., Thies, M., & Maier, A. (2024). Neural Network-based Sinogram Upsampling in Real-measured CT Reconstruction. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024. BVM 2024 (pp. 310-315). Erlangen, DE: Wiesbaden: Springer Vieweg.

MLA:

Augustin, Lena, et al. "Neural Network-based Sinogram Upsampling in Real-measured CT Reconstruction." Proceedings of the German Conference on Medical Image Computing, BVM 2024, Erlangen Ed. Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Wiesbaden: Springer Vieweg, 2024. 310-315.

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