Description
Leader of the group Computational Imaging: Joost Batenburg.
Our research group is developing the next generation of 3D imaging – enabling scientists to look further into objects of all kinds. Based on mathematics, algorithms and numerical solution techniques, our approach is interdisciplinary, combining aspects of mathematics, computer science and physics. We pride ourselves on the versatility of our solutions, and our algorithms can be applied to a wide range of imaging in science, medicine and industry. In Computational Imaging, it’s our goal to constantly push the boundaries of research. By combining advanced image acquisition, parameter estimation, and discrete tomography algorithms for example, we are able to develop workflows for 3D electron microscopy at atomic resolution.
News
Can CT Scans Be Used to Quickly and Accurately Diagnose COVID-19?
CWI’s Computational Imaging group joins research collaboration focusing on improving the accuracy of CT scan diagnosis
Making the invisible visible
CT machines are becoming the standard tool for looking inside objects of all kinds in research and industry. The FleX-ray Lab at CWI is making this type of imaging more accessible to math and computer science researchers. It's also drawing interest from the art, history, and the social sciences community.
CWI starts research project within NWO programme Smart Solutions for Horti- and Agriculture
A consortium led by Joost Batenburg of CWI’s Computational Imaging group has been awarded a grant of EUR 800.000 by the board of the NWO domain science.
EU grant for CWI and UCL to improve X-Ray scanning at lower doses with AI
CWI and UCL received an EU ATTRACT grant to deploy and develop AI techniques to improve X-ray CT scanning in such a way that much lower doses of radiation can be used to obtain high-resolution images. The project started in July 2019.
Members
- Vladyslav Andriiashen
- Joost Batenburg
- Francien Bossema
- Sophia Coban
- Poulami Ganguly
- Adriaan Graas
- Allard Hendriksen
- Ajinkya Kadu
- Rien Lagerwerf
- Tristan van Leeuwen
- Robert van Liere
- Felix Lucka
- Willem Jan Palenstijn
- Daniël Pelt
- Georgios Pilikos
- Richard Schoonhoven
- Dirk Schut
- Dzemila Sero
- Mathé Zeegers
Associated Members
Publications
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Rens, E.G, Zeegers, M.T, Rabbers, I, Szabó, A, & Merks, R.M.H. (2020). Autocrine inhibition of cell motility can drive epithelial branching morphogenesis in the absence of growth. Philosophical Transactions of the Royal Society B, 375(1807). doi:10.1098/rstb.2019.0386
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Buurlage, J. (2020, July). Real-time tomographic reconstruction.
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Kostenko, A, Palenstijn, W.J, Coban, S.B, Hendriksen, A.A, van Liere, R, & Batenburg, K.J. (2020). Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX, 11. doi:10.1016/j.softx.2019.100364
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McDonald, S.A, Coban, S.B, Sottos, N.R, & Withers, P.J. (2019). Tracking capsule activation and crack healing in a microcapsule-based self-healing polymer. Nature Scientific Reports, 9(1). doi:10.1038/s41598-019-54242-7
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Bender, H, Richard, O, Kundu, P, Favia, P, Zhong, Z, Palenstijn, W.J, … Schoenmakers, R. (2019). Combined STEM-EDS tomography of nanowire structures. Semiconductor Science and Technology, 34(11). doi:10.1088/1361-6641/ab4840
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Huynh, N, Lucka, F, Zhang, E, Betcke, M, Arridge, S, Beard, P, & Cox, B.T. (2019). Single-pixel camera photoacoustic tomography. Journal of Biomedical Optics, 24(12). doi:10.1117/1.JBO.24.12.121907
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Minnema, J, van Eijnatten, M.A.J.M, Hendriksen, A.A, Liberton, N.P.T.J, Pelt, D.M, Batenburg, K.J, … Wolff, J. (2019). Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network. Medical Physics. doi:10.1002/mp.13793
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Djurabekova, N, Goldberg, A, Hauptmann, A, Hawkes, D, Long, G, Lucka, F, & Betcke, M. (2019). Application of Proximal Alternating Linearized Minimization (PALM) and inertial PALM to dynamic 3D CT. In Proceedings of the 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. doi:10.1117/12.2534827
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Rimpiläinen, V, Koulouri, A, Lucka, F, Kaipio, J., & Wolters, C.H. (2019). Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity. NeuroImage, 188, 252–260. doi:10.1016/j.neuroimage.2018.11.058
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Buurlage, J, Bisseling, R.H, & Batenburg, K.J. (2019). A geometric partitioning method for distributed tomographic reconstruction. Parallel Computing, 81, 104–121. doi:10.1016/j.parco.2018.12.007
Software
Current projects with external funding
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Machine learning for large 3D tomographic images ()
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Mathematics and Algorithms for 3D Imaging of Dynamic Processes ()
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Non-destructive 3D spectral imaging: applications in the poultry industry ()
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PPS contract Planmeca ()
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Real-Time 3D Tomography ()
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Cambridge G102101: HPV mediated cervical lesions
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CT for Art: from Images to Patterns (IMPACT4Art)
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MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING)
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Translation-Driven Development of Deep Learning for Simultaneous Tomographic Image Reconstruction and Segmentation (None)
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Deep learning and compressed sensing for ultrasonic nondestructive testin (PPS Applus RTD)
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Universal Three-dimensiOnal Passport for process Individualization in Agriculture (UTOPIA)
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Enabling X-ray CT based Industry 4.0 process chains by training Next Generation research experts (xCTing)
Related partners
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Fraunhofer Gesellschaft
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Naturalis
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Rijksmuseum Amsterdam
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Universiteit Wageningen
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University of Cambridge
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GREEFA
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Katholieke Universiteit Leuven
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Rheinisch-Westfaelische Technische Hochschule Aachen
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Universiteit Utrecht