Publication list

Google scholar



Ouyang, R., Costa, A. R., Cassidy, C. K., ... & Briegel, A. (2022). High-resolution reconstruction of a Jumbo-bacteriophage infecting capsulated bacteria using hyperbranched tail fibers. Nature Communications, 13, 7241.

Sieverts, M., Obata, Y., Rosenberg, J. L., Woolley, W., Parkinson, D. Y., Barnard, H. S., ... & Acevedo, C. (2022). Unraveling the effect of collagen damage on bone fracture using in situ synchrotron microtomography with deep learning. Nature Communications Materials, 3(1), 1-13.

Segev-Zarko, L. A., Dahlberg, P. D., Sun, S. Y., Pelt, D. M., Kim, C. Y., Egan, E. S., ... & Boothroyd, J. C. (2022). Cryo-electron tomography with mixed-scale dense neural networks reveals key steps in deployment of Toxoplasma invasion machinery. PNAS nexus, 1(4), pgac183.

Shiraz, A., Egawa, N., Pelt, D. M., Crawford, R., Nicholas, A. K., Romashova, V., ... & Doorbar, J. (2022). Cervical cell lift: A novel triage method for the spatial mapping and grading of precancerous cervical lesions. EBioMedicine, 82, 104157.

Kim, J., Pelt, D. M., Kagias, M., Stampanoni, M., Batenburg, K. J., & Marone, F. (2022). Tomographic reconstruction of the small-angle x-ray scattering tensor with filtered back projection. Physical Review Applied, 18(1), 014043.

Zeegers, M. T., van Leeuwen, T., Pelt, D. M., Coban, S. B., van Liere, R., Batenburg, K.J. (2022). A tomographic workflow to enable deep learning for X-ray based foreign object detection. Expert Systems with Applications, 206, 117768.

Vădineanu, Ș., Pelt, D. M., Dzyubachyk, O., & Batenburg, J. (2022). An Analysis of the Impact of Annotation Errors on the Accuracy of Deep Learning for Cell Segmentation. In Medical Imaging with Deep Learning 2022.

Pelt, D. M., Roche i Morgó, O., Maughan Jones, C., Olivo, A., & Hagen, C. K. (2022). Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data. Scientific Reports, 12(1), 1-13.

Pelt, D. M., Hendriksen, A. A., & Batenburg, K. J. (2022). Foam-like phantoms for comparing tomography algorithms. Journal of Synchrotron Radiation, 29(1).


Hendriksen, A. A. et. al. (2021). Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python. Optics Express, 29(24), 40494-40513.

Ganguly, P. S., Pelt, D. M., Gürsoy, D., De Carlo, F., & Batenburg, K. J. (2021). Improving reproducibility in synchrotron tomography using implementation-adapted filters. Journal of Synchrotron Radiation 28(5), 1583-1597

Roche i Morgó, O. et. al. (2021). Exploring the potential of cycloidal computed tomography for advancing intraoperative specimen imaging. Developments in X-Ray Tomography XIII. Vol. 11840. International Society for Optics and Photonics, 2021.

Skorikov, A., Heyvaert, W., Albecht, W., Pelt, D. M., & Bals, S. (2021). Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles. Nanoscale.

Hendriksen, A. A., Bührer, M., Leone, L., Merlini, M., Vigano, N., et al. (2021). Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data. Scientific Reports, 11(1), 11895.


Pelt, D. M. (2020). Deep Learning: Tackling the challenges of bioimage analysis. Elife, 9, e64384.

Lagerwerf, M. J., Pelt, D. M., Palenstijn, W. J., & Batenburg, K. J. (2020). A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks. Journal of Imaging, 6(12), 135.

Zeegers, M. T., Pelt, D. M., van Leeuwen, T., van Liere, R., & Batenburg, K. J. (2020). Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning. Journal of Imaging, 6(12), 132.

Pelt, D., Maughan-Jones, C., Morgo, R. I., Olivo, A., & Hagen, D. (2020). Rapid and flexible high-resolution scanning enabled by cycloidal computed tomography and convolutional neural network (CNN) based data recovery. In 6th International Conference on Image Formation in X-Ray Computed Tomography.

Schoonhoven, R., Buurlage, J. W., Pelt, D. M., & Batenburg, K. J. (2020). Real-time segmentation for tomographic imaging. In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 1-6.

Hendriksen, A. A., Pelt, D. M., & Batenburg, K. J. (2020). Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography. IEEE Transactions on Computational Imaging, 6, 1320-1335.

Flenner, S. et. al. (2020). Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast in situ experiments. Journal of Synchrotron Radiation, 27(5).

Vanrompay, H. et. al. (2020). Real‐Time Reconstruction of Arbitrary Slices for Quantitative and In Situ 3D Characterization of Nanoparticles. Particle & Particle Systems Characterization, 2000073.

Parkinson, D. Y. et al. (2020). Tomographic Reconstruction for Synchrotron Tomography. In Handbook on Big Data and Machine Learning in the Physical Sciences, 65-82.

Chang, H. et al. (2020). Building Mathematics, Algorithms, and Software for Experimental Facilities. In Handbook on Big Data and Machine Learning in the Physical Sciences, 189-240.


Buurlage, J. W., Marone, F., Pelt, D. M., Palenstijn, W. J., Stampanoni, M., Batenburg, K. J., & Schlepütz, C. M. (2019). Real-time reconstruction and visualisation towards dynamic feedback control during time-resolved tomography experiments at TOMCAT. Scientific Reports, 9(1), 1-11.

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.


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.


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.


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.


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).


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).


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.


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