Description
Leader of the group Computational Imaging: Tristan van Leeuwen.
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
No vacancies currently.
News
Second release ASTRA toolbox
The new version of ASTRA, co-developed at CWI, offers improvements in compatibility and interoperability with other toolboxes.
CWI podcast: How the FleX-ray scanner reveals art secrets
The science podcast "How CWI's FleX-ray scanner reveals art secrets" is now online. CWI PhD candidate Francien Bossema (CI) and Rijksmuseum professor Erma Hermens talk to science journalist Bennie Mols about our special scanner.
CT scan by CWI reveals hidden double-panel painting from Rubens studio
CT scans of a Rijksmuseum panel painting in CWI’s FleX-ray laboratory have revealed a surprising insight: it turns out it was not painted on oak, as was always assumed, but on tropical wood.
IMPACT4ART: the interplay between scientists, art historians, conservators and the general public
The wooden sculpture shown in the green oval is surrounded by mystery: How old is this wooden sculpture? Where and by whom was it made? Mathematics and computer science can help to answer such questions.
Members
- Vladyslav Andriiashen
- Joost Batenburg
- Francien Bossema
- Hamid Fathi
- Adriaan Graas
- Maximilian Kiss
- Paul van Laar
- Tristan van Leeuwen
- Robert van Liere
- Felix Lucka
- Jan Matula
- Willem Jan Palenstijn
- Richard Schoonhoven
- Ryan Schoop
- Dirk Schut
- Dzemila Sero
- Alexander Skorikov
- Roozbeh Valadian
- Tianyuan Wang
- Mathé Zeegers
Associated Members
Publications
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Hendriksen, A.A, Schut, D, Palenstijn, W.J, Viganò, N.R, Kim, J, Pelt, D.M, … Batenburg, K.J. (2021). Tomosipo: fast, flexible, and convenient 3D tomography for complex scanning geometries in Python. doi:10.1364/OE.439909
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Pilikos, G, Horchens, L, van Leeuwen, T, & Lucka, F. (2021). Deep learning for multi-view ultrasonic image fusion. In IEEE International Ultrasonics Symposium. doi:10.1109/IUS52206.2021.9593507
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Orozco, R, Siahkoohi, A, Rizzuti, G, van Leeuwen, T, & Herrmann, F.J. (2021). Photoacoustic imaging with conditional priors from normalizing flows. In Proceedings NeurIPS (Annual Conference on Neural Information Processing Systems).
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Izzatullah, M, van Leeuwen, T, & Peter, D. (2021). Langevin dynamics Markov Chain Monte Carlo solution for seismic inversion. In 82nd EAGE Annual Conference & Exhibition (Vol. 2021, pp. 1–5). doi:10.3997/2214-4609.202010496
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Ganguly, P.S, Pelt, D.M, Gürsoy, D, De Carlo, F, & Batenburg, K.J. (2021). Improving reproducibility in synchrotron tomography using implementation-adapted filters. In Journal of Synchrotron Radiation (Vol. 28, pp. 1583–1597). doi:10.1107/S1600577521007153
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Domínguez-Delmás, M, Bossema, F.G, Dorscheid, J, Coban, S.B, Hall-Aquitania, M, Batenburg, K.J, & Hermens, E. (2021). X-ray computed tomography for non-invasive dendrochronology reveals a concealed double panelling on a painting from Rubens’ studio. PLoS ONE, 16(8). doi:10.1371/journal.pone.0255792
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Pan, B, Arridge, S, Lucka, F, Cox, B.T, Huynh, N, Beard, P, … Betcke, M. (2021). Photoacoustic reconstruction using sparsity in Curvelet frame: Image versus data domain. IEEE Transactions on Computational Imaging, 7, 879–893. doi:10.1109/TCI.2021.3103606
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Minnema, J, van Eijnatten, M.A.J.M, Der Sarkissian, H.H, Doyle, S, Koivisto, J, Wolff, J, … Batenburg, K.J. (2021). Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction. Physics in Medicine and Biology, 66(13). doi:10.1088/1361-6560/ac09a1
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Andriiashen, V, & Kozhevnikov, D. (2021). Development of the projection-based material decomposition algorithm for Multienergy CT. IEEE Transactions on Radiation and Plasma Medical Sciences, 5(4), 517–527. doi:10.1109/TRPMS.2020.3022479
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Andriiashen, V, van Liere, R, van Leeuwen, T, & Batenburg, K.J. (2021). Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry. Journal of Imaging, 7(7). doi:10.3390/jimaging7070104
Software
ASTRA Toolbox: Commercial-class software for tomography imaging
The ASTRA Toolbox is a MATLAB and Python platform providing scalable, high-performance GPU primitives for 2D and 3D tomography, including building blocks for advanced reconstruction algorithms.
RECAST3D: a real-time visualization platform for tomographic imaging
RECAST3D provides real-time tomographic reconstruction and visualization of arbitrarily oriented 2D slices in a 3D volume.
Current projects with external funding
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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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Cambridge RG99590 AIO cancer imaging optimisation (Cancer Imaging Optimisation)
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the Center for Optimal, Real-Time Machine Studies of the Explosive Universe (CORTEX)
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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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IntACT: Visualisation of Interior of Art objects through CT scans (NICAS grant)
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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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Team Science Award
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Universal Three-dimensiOnal Passport for process Individualization in Agriculture (UTOPIA)
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Mathematics and Algorithms for 3D Imaging of Dynamic Processes / OIO positie Graas (Wiskundeclusters)
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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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ABN AMRO Bank
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Fraunhofer Gesellschaft
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IBM
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Katholieke Universiteit Nijmegen
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Naturalis
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Netherlands eScience Center
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NIKHEF
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Rijksmuseum Amsterdam
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Universiteit Wageningen
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University of Cambridge
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Nederland Instituut voor Radio Astronomie
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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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SURFsara B.V.
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Universiteit Utrecht
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Universiteit van Amsterdam