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17 October 2022 Deep learning-based detector row upsampling for clinical spiral CT
Jan Magonov, Julien Erath, Joscha Maier, Eric Fournié, Karl Stierstorfer, Marc Kachelrieß
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Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 1230407 (2022) https://doi.org/10.1117/12.2647101
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States
Abstract
Due to longitudinal undersampling multislice spiral computed tomography (MSCT) scans may suffer from windmill artifacts in reconstructed images. To fulfill the sampling condition and achieve double sampling in z-direction, some CT scanners use the z-flying focal spot (zFFS) technique, a hardware-based solution that effectively doubles the number of detector rows. To obtain a software-based solution we developed a convolutional neural network that is trained in a supervised manner with clinical projection raw data that were acquired with zFFS enabled. We presented this approach as the row interpolation with deep learning (RIDL) network. In this work we simplified the network architecture, extended the clinical dataset and generated an experimental synthetic dataset consisting of two-dimensional projection data. We were able to observe a reduction in windmill artifacts for both datasets used for training. Especially the synthetic dataset is very promising as we could observe a superior reduction of artifacts with this dataset.
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Jan Magonov, Julien Erath, Joscha Maier, Eric Fournié, Karl Stierstorfer, and Marc Kachelrieß "Deep learning-based detector row upsampling for clinical spiral CT", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 1230407 (17 October 2022); https://doi.org/10.1117/12.2647101
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KEYWORDS
Computed tomography
Collimation
Network architectures
X-ray computed tomography
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