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.
Vacancies
Postdoc on the subject of Pattern Recognition and Analysis for Technical Art History
Centrum Wiskunde & Informatica (CWI) has a vacancy in the Computational Imaging research group for a talented Postdoc on the subject of Pattern Recognition and Analysis for Technical Art History
News
Faster computation of high-quality 3D images
PhD student Daniël Pelt of CWI has developed new algorithms for computer tomography (CT) for fast computation of high-quality three-dimensional images. This increases the practical use of this technology in areas such as material sciences and biomedical research.
Vici grant for Joost Batenburg
Researcher Joost Batenburg of CWI has been awarded a Vici grant from the Netherlands Organisation for Scientific Research (NWO) for his proposal 'Real-Time 3D Tomography'. With the 1.5 million euro grant, Batenburg can develop his own research line in the next five years.
CWI reduces perturbations in tomography
Researcher Folkert Bleichrodt of CWI has developed new methods to improve the applicability of tomography. These methods reduce unwanted perturbations. This allows for applying tomography on a very small scale or based on a small number of measurements.
CWI on Belgian TV
CWI could be seen on the national Belgian TV, in the programme 'Iedereen beroemd' - 'Everybody Famous'. The item was broadcasted on Tuesday 3 February 2015 on channel Eén at 19.40h - and again after the late news. Broadcaster VRT visited CWI on 22 January.
Members
Associated Members
Publications
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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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van Liere, R. (2019). Flexibele CT scanner maakt interactieve 3D beelden - Bits & Chips- 8-1-2019.
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Buurlage, J.W, 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
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Zhong, Z. (2018, December 10). Reconstruction methods for combined HAADF-STEM and EDS tomography.
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Zeegers, M.T, Lucka, F, & Batenburg, K.J. (2018). A Multi-Channel DART algorithm. In Combinatorial Image Analysis (pp. 164–178). doi:10.1007/978-3-030-05288-1_13
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Tovey, R, Benning, M, Brune, C, Lagerwerf, M.J, Collins, S.M, Leary, R, … Schönlieb, C.-B. (2018). Directional sinogram inpainting for limited angle tomography. Inverse Problems. doi:10.1088/1361-6420/aaf2fe
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Zhong, Z, Palenstijn, W.J, Viganò, N.R, & Batenburg, K.J. (2018). Numerical methods for low-dose EDS tomography. Ultramicroscopy, 194, 133–142. doi:10.1016/j.ultramic.2018.08.003
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Kostenko, A, Andriiashen, V, & Batenburg, K.J. (2018). Registration-based multi-orientation tomography. Optics Express, 26(22), 28982–28995. doi:10.1364/OE.26.028982
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Lucka, F, Huynh, N, Betcke, M, Zhang, E, Beard, P, Cox, B.T, & Arridge, S. (2018). Enhancing compressed sensing 4D photoacoustic tomography by simultaneous motion estimation. SIAM Journal on Imaging Sciences, 11(4), 2224–2253. doi:10.1137/18M1170066
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Hauptmann, A, Cox, B.T, Lucka, F, Huynh, N, Betcke, M, Beard, P, & Arridge, S. (2018). Approximate k-space models and deep learning for fast photoacoustic reconstruction. In Machine Learning for Medical Image Reconstruction (pp. 103–111). doi:10.1007/978-3-030-00129-2_12
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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Rapid 3D Printing through full-field volumetric photolithography
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Real-Time 3D Tomography
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The See-Through Museum
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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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Volumetric medical x-ray imaging at extremely low dose (VOXEL)
Related partners
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Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
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CNR Pisa
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COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
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IMAGINE OPTIC
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Meyn Food Processing Technology B.V.
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Naturalis
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Planmeca
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Rijksmuseum Amsterdam
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Universidad Politécnica de Madrid