A collection of 131 CT datasets of pieces of modeling clay containing stones - Part 4 of 5

Summary This submission contains a collection of 131 CT scans of pieces of modeling clay (Play-Doh) with various numbers of stones inserted. The submission is intended as raw supplementary material to reproduce the CT reconstructions and subsequent results in the paper titled "A tomographic workflow enabling deep learning for X-ray based foreign object detection" [Zeegers 2022]. This submission consists of three parts in total. Parts The 131 CT scans are divided into 5 separate submissions: Part 1 of 5: 001-028: 10.5281/zenodo.5866228 Part 2 of 5: 029-056: 10.5281/zenodo.5866322 Part 3 of 5: 057-084: 10.5281/zenodo.5866363 Part 4 of 5: 085-111: 10.5281/zenodo.5866365 (this upload) Part 5 of 5: 112-131: 10.5281/zenodo.5866367 Description Sample information The samples are modeling clay (Play-Doh, Hasbro, RI, USA) with various numbers of pieces of gravel included. In total 131 samples are prepared, of which 20 samples contain 5-8 inserted stones, 3 samples contain three stones, 35 contain two stones, 62 contain one stone and 11 contain no stones. The stones have an average diameter of ca. 7mm (ranging from 3mm to 11mm). The Play-Doh is remolded for every sample. Apparatus The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020]. Scanning setup For each sample, 1800 radiographs are collected by rotating the sample over 360 degrees in a circular and continuous motion. A peak voltage of 90kV is used, and the target power is set to 20W. The distance between the source and detector is 69.80 cm and the distance between the source and the object is 44.14 cm. An exposure time of 20 ms is used for each projection. Experimental plan This data is the result of a demonstration of a workflow to collect annotated data for supervised machine learning for X-ray based object detection. The ground truth locations are retrieved by tomographic reconstruction, segmentation and virtual projections with the same acquisition angles. A detailed description for the workflow to obtain a training dataset is given in [Zeegers 2022]. Technical details All projections are unprocessed files, except that a binning been applied FleX-ray lab software. The resulting image sizes are 956x760. Flatfield images (averaged over 10 pre and 10 post radiographs) and darkfield images (averaged over 10 pre and 10 post images) are included with each object. All images are stored in .tif format. The data for samples with 0-3 stones are contained in parts 1 to 4, while the samples with 5-8 stones constitute part 5. The size of the completely unpacked dataset (all 5 parts) is ca. 343.5 GB. The processed data (with generated ground truth) is made available in another (smaller) submission for object detection purposes: https://zenodo.org/record/5681008 Additional Links These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: https://www.cwi.nl/research/groups/computational-imaging Contact details zeegers [at] cwi [dot] nl Acknowledgments The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory. References [Zeegers 2022] M. T. Zeegers, T. van Leeuwen, D. M. Pelt, S. B. Coban, R. van Liere, K. J. Batenburg, "A tomographic workflow to enable deep learning for X-ray based foreign object detection", 2022 (submitted) [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018. If you use (parts of) this data in a publication, we would appreciate it if you would refer to the first article.## 数据集概述 本提交包含131组嵌入不同数量石块的培乐多（Play-Doh）造型黏土计算机断层扫描（CT）图像。本数据集作为原始补充材料，用于复现论文《支持深度学习的X射线异物检测层析成像工作流》[Zeegers 2022]中的CT重建及后续实验结果。本次提交所属的完整数据集共分为5个独立部分： 1. 第1部分（共5部分）：001-028，对应链接：10.5281/zenodo.5866228 2. 第2部分（共5部分）：029-056，对应链接：10.5281/zenodo.5866322 3. 第3部分（共5部分）：057-084，对应链接：10.5281/zenodo.5866363 4. 第4部分（共5部分）：085-111，即本上传提交，对应链接：10.5281/zenodo.5866365 5. 第5部分（共5部分）：112-131，对应链接：10.5281/zenodo.5866367 ## 样本信息 本次实验的样本为嵌入不同数量砾石的培乐多（Play-Doh）造型黏土，共制备131个样本：其中20个样本嵌入5~8块石块，3个样本嵌入3块石块，35个样本嵌入2块石块，62个样本嵌入1块石块，11个样本未嵌入石块。石块平均直径约7mm（直径范围3mm~11mm）。每个样本均重新塑形培乐多黏土。 ## 采集设备 本数据集采集于荷兰阿姆斯特丹数学与计算机科学研究中心（Centrum Wiskunde & Informatica, CWI）的FleX-ray实验室，该实验室由TESCAN-XRE搭建。CT扫描仪采用锥束微焦点多色X射线点光源，搭配1944×1536像素、14位平板探测器（Dexela1512NDT）。详细参数可参见文献[Coban 2020]。 ## 扫描设置 每个样本通过360度连续圆周旋转，采集1800张射线照相图像。扫描参数为：峰值电压90kV，目标功率20W。射线源与探测器间距69.80cm，射线源与样本间距44.14cm。单次投影曝光时间为20ms。 ## 实验方案 本数据集用于演示针对X射线目标检测的监督式机器学习标注数据采集工作流。通过采用与实际采集一致的扫描角度，开展层析重建、图像分割与虚拟投影操作，从而获取样本的真实标注（ground truth）位置。获取训练数据集的详细工作流可参见文献[Zeegers 2022]。 ## 技术细节 所有投影图像均为未经过额外处理的原始文件，仅通过FleX-ray实验室软件应用了合并（binning）处理，最终图像尺寸为956×760。每个样本均附带平场图像（flatfield image，对10张前置与10张后置射线照相图像取平均）与暗场图像（darkfield image，对10张前置与10张后置图像取平均）。所有图像均以.tif格式存储。 嵌入0~3块石块的样本数据包含于第1至第4部分，而嵌入5~8块石块的样本数据属于第5部分。完整解压后的5部分数据集总容量约为343.5GB。针对目标检测任务的带生成式真实标注的处理后数据集，已通过另一项容量更小的提交发布，链接为：https://zenodo.org/record/5681008。 ## 附加链接 本数据集由荷兰阿姆斯特丹CWI计算成像组制作，相关链接：https://www.cwi.nl/research/groups/computational-imaging ## 联系方式 官方联系邮箱：[e-mail] ## 致谢 作者感谢荷兰科学研究组织（NWO）项目编号639.073.506的资助。同时感谢TESCAN-XRE NV对FleX-ray实验室的合作与支持。 ## 参考文献 [Zeegers 2022] M. T. Zeegers、T. van Leeuwen、D. M. Pelt、S. B. Coban、R. van Liere、K. J. Batenburg，《支持深度学习的X射线异物检测层析成像工作流》，2022年（已投稿） [Coban 2020] S. B. Coban、F. Lucka、W. J. Palenstijn、D. Van Loo、K. J. Batenburg，《FleX-ray实验室的探索成像技术及其实现》，《Journal of Imaging》，第6卷第18期，2020年，DOI: 10.3390/jimaging6040018 若您在出版物中使用本数据集（或其部分内容），敬请引用上述第一篇文献。