Daniël M. Pelt

Employment

2017 - now Postdoctoral Researcher

2016 - 2017 Postdoctoral Fellow

2012 - 2016 Ph.D. Student

2010 - 2011 Scientific Programmer

Education

2016 Ph. D.

2010 Master Scientific Computing (cum laude)

2008 Bachelor Physics and Astronomy

2004 Pre-university secondary education

Selected publications

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.

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

Academic activities

Scholarships/prizes

EU ATTRACT 2019 (co-I), 100k euro

NWO Veni Grant 2018 (PI), 250k euro

IOP Outstanding Reviewer Award 2017

Research Aide Appointment (PI), Argonne National Laboratory, 4800 dollar

Short Term Scientific Mission (PI), EU COST Action MP-1207, 1100 euro

Ad hoc reviewer

Nature Scientific Reports, eLife, IEEE Transactions on Image Processing, IEEE Transactions on Compuational Imaging, Journal of Synchrotron Radiation, Measurement Science and Technology, …

Working visits

01/2018 Swiss Light Source, Paul Sherrer Institute, Villigen, Switzerland. One week.

06/2015 Advanced Photon Source, Argonne National Laboratory, Argonne, Illinois, USA. Five weeks.

03/2014 Swiss Light Source, Paul Sherrer Institute, Villigen, Switzerland. Two weeks.

05/2013 European Synchrotron Radiation Facility, Grenoble, France. Two weeks.

Teaching

Guest lecturer for the 2015 Parallel Algorithms course, taught by prof. dr. Rob H. Bisseling at Utrecht University.

Teacher at the ASTRA toolbox session during “Workshop on Experimental and Computational Tomography” (May 2015, Grenoble, France).

Teacher at the “Training school on using the ASTRA toolbox for X-ray tomography” (March 2015, Antwerp, Belgium).

Teacher at the “Unleashing the ASTRA Tomography Toolbox” workshop (April 2014, Antwerp, Belgium).

Talks

08/2019 Lombard IL, USA, ICXOM 25.

06/2019 Amsterdam, The Netherlands, Code Sprint: Deep Learning for High Resolution 3D Tomographic Reconstruction.

05/2019 Milan, Italy, Meeting on Tomography and Applications.

04/2019 Lund, Sweden, Inverse problems in X-ray phase retrieval and tomography.

11/2018 Berkeley CA, USA, CAMERA Workshop Algorithms and Software for Tomographic Reconstruction for Beamlines.

04/2018 Groningen, The Netherlands, Imaging Informatics Colloquium.

01/2018 Villigen, Switzerland, Meeting at Swiss Light Source, Paul Scherrer Institute.

11/2017 Berkeley CA, USA, CAMERA Workshop Algorithms and Software for Tomographic Reconstruction for Beamlines.

11/2016 Berkeley CA, USA, CAMERA Workshop: Algorithms for Tomographic Reconstruction: State-of-the-Art and Future Goals.

10/2016 Berkeley CA, USA, Advanced Light Source User Meeting 2016.

05/2016 Argonne IL, USA, 2016 APS/CNM Users Meeting 2016.

02/2016 Berkeley CA, USA, Advanced Light Source Tomography Beamline Seminar.

11/2015 Antwerp, Belgium, International Workshop on Industrial Tomography 2015.

09/2015 London, UK, Focused Mini-Workshop on Differential Phase Contrast Tomography.

06/2015 Argonne IL, USA, Laboratory for Advanced Numerical Simulations(LANS) Informal Seminar, Mathematics & Computer Science (MCS) Division, Argonne National Laboratory.

06/2015 Argonne IL, USA, Coffee seminar of the Imaging and Microscopy Groups, Advanced Photon Source, Argonne National Laboratory.

06/2015 Newport RI, USA, the 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine.

05/2015 Grenoble, France, Workshop on Experimental and Computational Tomography.

04/2015 Milan, Italy, Meeting on Tomography and Applications.

12/2014 Trieste, Italy, Advances in X-ray Imaging workshop.

11/2014 Antwerp, Belgium, Meeting at Electron microscopy for materials science (EMAT), University of Antwerp.

05/2014 Phoenix AZ, USA, 28th IEEE International Parallel & Distributed Processing Symposium.

05/2014 Hong Kong, China, SIAM Conference on Imaging Science.

03/2014 Villigen, Switzerland, Meeting at Swiss Light Source, Paul Scherrer Institute.

02/2014 Antwerp, Belgium, Meeting at iMinds - Vision Lab, University of Antwerp.

12/2013 Antwerp, Belgium, Meeting at Electron microscopy for materials science (EMAT), University of Antwerp.

07/2013 Ghent, Belgium, 1st International Conference on Tomography of Materials and Structures.

05/2013 Grenoble, France, ESRF SciSoft coffee meeting.

12/2012 Paris, France, Workshop on X-ray tomography reconstruction.

Publication list

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.