Publication list

Google scholar

ResearchGate

2019

Minnema, J. et al. (2019). Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network. Medical Physics.

Hendriksen, A. A., Pelt, D. M., Palenstijn, W. J., Coban, S. B., & Batenburg, K. J. (2019). On-the-Fly Machine Learning for Improving Image Resolution in Tomography. Applied Sciences, 9(12), 2445.

2018

Pelt, D. M., Batenburg, K. J., & Sethian, J. A. (2018). Improving tomographic reconstruction from limited data using mixed-scale dense convolutional neural networks. Journal of Imaging, 4(11), 128.

Parkinson, D. Y. et al. (2018). Achieving fast high-resolution 3D imaging by combining synchrotron x-ray microCT, advanced algorithms, and high performance data management. In Image Sensing Technologies: Materials, Devices, Systems, and Applications V (Vol. 10656, p. 106560S).

Pelt, D. M., & Parkinson, D. Y. (2018). Ring artifact reduction in synchrotron x-ray tomography through helical acquisition. Measurement Science and Technology, 29(3), 034002.

De Carlo, F. et al. (2018). TomoBank: a tomographic data repository for computational x-ray science. Measurement Science and Technology, 29(3), 034004.

Pelt, D. M., & Sethian, J. A. (2018). A mixed-scale dense convolutional neural network for image analysis. Proceedings of the National Academy of Sciences, 115(2), 254-259.

2017

Parkinson, D. Y. et al. (2017). Machine learning for micro-tomography. In Developments in X-Ray Tomography XI (Vol. 10391, p. 103910J).

Perciano, T. et al. (2017). Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics. Journal of synchrotron radiation, 24(5), 1065-1077.

Pelt, D. M., & Andrade, V. (2017). Improved tomographic reconstruction of large-scale real-world data by filter optimization. Advanced Structural and Chemical Imaging, 2(1), 17.

2016

Pelt, D. M. (2016). Filter-based reconstruction methods for tomography (Doctoral dissertation).

Pelt, D. M., Gürsoy, D., Palenstijn, W. J., Sijbers, J., De Carlo, F., & Batenburg, K. J. (2016). Integration of TomoPy and the ASTRA toolbox for advanced processing and reconstruction of tomographic synchrotron data. Journal of synchrotron radiation, 23(3), 842-849.

2015

Bladt, E., Pelt, D. M., Bals, S., & Batenburg, K. J. (2015). Electron tomography based on highly limited data using a neural network reconstruction technique. Ultramicroscopy, 158, 81-88.

Pelt, D. M., & Bisseling, R. H. (2015). An exact algorithm for sparse matrix bipartitioning. Journal of Parallel and Distributed Computing, 85, 79-90.

Janssens, E., Pelt, D. M., De Beenhouwer, J., Van Dael, M., Verboven, P., Nicolai, B., & Sijbers, J. (2015). Fast neural network based X-ray tomography of fruit on a conveyor belt. In Frutic Italy 2015: 9th nut and vegetable production engineering symposium (Vol. 44, pp. 181-186).

Pelt, D. M., & Batenburg, K. J. (2015). Accurately approximating algebraic tomographic reconstruction by filtered backprojection. In Proceedings of The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (pp. 158-161).

2014

Pelt, D. M., & Batenburg, K. J. (2014). Improving filtered backprojection reconstruction by data-dependent filtering. IEEE Transactions on Image Processing, 23(11), 4750-4762.

Pelt, D. M., & Bisseling, R. H. (2014). A medium-grain method for fast 2D bipartitioning of sparse matrices. In Parallel and Distributed Processing Symposium, 2014 IEEE 28th International (pp. 529-539).

2013

Pelt, D. M., Sijbers, J., & Batenburg, K. J. (2013). Fast tomographic reconstruction from highly limited data using artificial neural networks. In 1st International Conference on Tomography of Materials and Structures (ICTMS) (pp. 109-112).

Pelt, D. M., & Batenburg, K. J. (2013). Fast tomographic reconstruction from limited data using artificial neural networks. IEEE Transactions on Image Processing, 22(12), 5238-5251.

2009

Filion, L. et al. (2009). Efficient method for predicting crystal structures at finite temperature: Variable box shape simulations. Physical review letters, 103(18), 188302.