A cone-beam X-ray computed tomography data collection designed for machine learning

Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation. Measurement(s)JuglansTechnology Type(s)computed tomographyFactor Type(s)source height Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9912836

[1]  J. Hsieh,et al.  A practical cone beam artifact correction algorithm , 2000, 2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149).

[2]  Jan Sijbers,et al.  Fast and flexible X-ray tomography using the ASTRA toolbox. , 2016, Optics express.

[3]  C. McCollough.  TU-FG-207A-04: Overview of the Low Dose CT Grand Challenge. , 2016, Medical physics.

[4]  Xiao Han,et al.  Artifact reduction in short-scan CBCT by use of optimization-based reconstruction , 2016, Physics in medicine and biology.

[5]  William R B Lionheart,et al.  SparseBeads data: benchmarking sparsity-regularized computed tomography , 2017 .

[6]  Esa Niemi,et al.  Tomographic X-ray data of a walnut , 2015, 1502.04064.

[7]  Maureen van Eijnatten,et al.  Effective Radiation Dose in the Wrist Resulting from a Radiographic Device, Two CBCT Devices and One MSCT Device: A Comparative Study , 2018, Radiation protection dosimetry.

[8]  Martin J. Blunt,et al.  Time-resolved synchrotron X-ray micro-tomography datasets of drainage and imbibition in carbonate rocks , 2018, Scientific Data.

[9]  Jan Sijbers,et al.  TomoBank: a tomographic data repository for computational x-ray science , 2018 .

[10]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[11]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[12]  Sophia B. Coban.  SophiaBeads Datasets Project Documentation and Tutorials , 2015 .

[13]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[14]  Jeffrey A. Fessler,et al.  Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning , 2019, Proceedings of the IEEE.

[15]  Jeffrey A. Fessler,et al.  Image Reconstruction is a New Frontier of Machine Learning , 2018, IEEE Transactions on Medical Imaging.

[16]  Antonin Chambolle,et al.  An introduction to continuous optimization for imaging , 2016, Acta Numerica.

[17]  Evon M. O. Abu-Taieh,et al.  Comparative Study , 2020, Definitions.

[18]  Günter Lauritsch,et al.  A factorization approach for cone-beam reconstruction on a circular short-scan , 2008, IEEE Transactions on Medical Imaging.

[19]  Kerstin Pingel,et al.  50 Years of Image Analysis , 2012 .

[20]  H. Tuy.  AN INVERSION FORMULA FOR CONE-BEAM RECONSTRUCTION* , 1983 .

[21]  Kees Joost Batenburg,et al.  Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks , 2018, J. Imaging.