We release tomographic scans of 21 walnuts with two levels of radiation dosage for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The dataset collected with higher dosage is referred to as the "good" dataset; and the other as the "noisy" dataset, as a way to distinguish between the two dosage levels.

The dataset are acquired using the custom built and highly flexible CT scanner, FlexRay Lab, developed by XRE NV and located at CWI. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1943-by-1535 pixels, 14-bit, flat detector panel. To reduce the sizes of the datasets the detector pixels were binned pair-wise to get 972-by-768 detector pixels.

All datasets were collected over a 360 degrees in circular and continuous motion with 501 projections distributed evenly over the full circle. The uploaded datasets are not normalized; a single dark and two (pre- and post-) flat fields are included for each scan. Projections for both sets were collected with 100 ms exposure time with the good data projections averaged over 5 takes, and no averaging was made for the noisy data. The tube settings for the good and noisy dataset were 70kV, 45W and 70kV, 20W, respectively. The total scanning time were 20 minutes for the good; 3 minutes for the noisy scan. Each dataset is packaged with the full list of data and scan settings files (in .txt format). These files contain the tube settings, scan geometry and full list of motor settings.

These dataset are produced by the Computational Imaging members at Centrum Wiskunde & Informatica (CI-CWI). For any useful Python/MATLAB scripts for FlexRay dataset, we refer the reader to our group's GitHub page.

For more information or guidance in using these dataset, please get in touch with m.j.lagerwerf [at] cwi.nl s.b.coban [at] cwi.nl or

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Lagerwerf, Marinus Jan, Coban, Sophia Bethany, & Batenburg, K. Joost. (2020). High-resolution cone-beam scan of twenty-one walnuts with two dosage levels. doi:10.5281/zenodo.3763412