Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5,000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.

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Scientific Data
Translation-Driven Development of Deep Learning for Simultaneous Tomographic Image Reconstruction and Segmentation , Mathematics and Algorithms for 3D Imaging of Dynamic Processes , Real-Time 3D Tomography
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Computational Imaging

Kiss, M., Coban, S., Batenburg, J., van Leeuwen, T., & Lucka, F. (2023). 2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning. Scientific Data, 10, 576:1–576:12. doi:10.1038/s41597-023-02484-6