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.
More information on the Computational Imaging group at CWI.
Vacancies
No vacancies currently.
News

An inspiring masterclass on machine learning for inverse problems
CWI organised a 2-day masterclass, as part of the CWI Semester Program on Data-driven methods for Inverse Problems. Participants from Groningen to Paris attented the event.

Algorithms can also learn without examples
Allard Hendriksen (CWI) developed an algorithm that improves X-ray images without having to learn from data from previous measurements. He defends his PhD thesis on 3 March at Leiden University.

CWI scanner revealing art secrets seeks new applications
CWI and Rijksmuseum are exploring the possibilties of CWI's 3D FleX-ray scanner together.

Developing Improved Breast Cancer Imaging Techniques
Breast Cancer is the most common cause of cancer death in women worldwide. Felix Lucka from the Computational Imaging group at CWI is part of a European research team that develops novel imaging techniques that will improve early detection and diagnosis in the future.
Current events
SECOND ANNUAL MEETING FOR THE DUTCH INVERSE PROBLEMS COMMUNITY
- 2022-11-10T09:30:00+01:00
- 2022-11-11T18:00:00+01:00
SECOND ANNUAL MEETING FOR THE DUTCH INVERSE PROBLEMS COMMUNITY
Start: 2022-11-10 09:30:00+01:00 End: 2022-11-11 18:00:00+01:00
The second annual meeting of the Dutch Inverse Problems Community will take place on 10-11 November 2022 in conference center de Werelt, Lunteren. A summary of last year’s event can be read here.
Registration
Registration will open soon. We are currently acquiring additional funding to keep registration fees as low as possible. We expect to be able to offer 2-day attendance for EUR 150 – 200 (including lunch, diner, and hotel), and 1-day attendance for EUR 25 – 50 (including lunch).
Preliminary program
Thursday November 10
Masterclasses on High Performance Computing and Inverse Problems (preliminary abstracts are included below). The masterclasses will take in place in parallel and aim to give an in-depth overview and hands-on experience on the topic.
Friday November 11
Scientific talks, highlighting recent developments in Inverse Problems from a mathematical and practical point of view. The talks are by invitation, and selected to give a balanced perspective on theory and applications of inverse problems from both junior and senior researchers.
1st workshop on AI and Mathematics - connecting the mathematics clusters
- 2022-06-09T09:00:00+02:00
- 2022-06-10T17:00:00+02:00
1st workshop on AI and Mathematics - connecting the mathematics clusters
Start: 2022-06-09 09:00:00+02:00 End: 2022-06-10 17:00:00+02:00
The first AIM workshop is organised by the Dutch Mathematics Clusters and its goal is to highlight the role of mathematics as a key enabling technology within the emerging field of scientific machine learning, and bring together researchers across mathematics.
Artificial Intelligence (AI) will have a growing impact on all sciences and business sectors, our private lives, and society as a whole. It is pre-eminently a multidisciplinary technology that connects scientists from a wide variety of research areas, from behavioural science and ethics to mathematics and computer science. Without downplaying the importance of its interdisciplinary nature, it is apparent that mathematics can and should play an active role. As Robert Dijkgraaf observed in NRC in May 2019: ''Artificial intelligence is in its adolescent phase, characterised by trial and error, self-aggrandisement, credulity and lack of systematic understanding''. Mathematics can contribute to this much-needed systematic understanding of AI and at the same time lay the ground work for further improvements.
In the two-day program we showcase recent research on the interface between mathematics and AI. There will be plenty of time for informal discussions, as well as strategic sessions.
The workshop is free of charge after registration, please register here.
If you have any questions, please contact us at: aim@nwo.nl
Accommodation
Hotel reservation can be booked directly at Hotel Casa for a corporate rate of 119 euro including breakfast. Please use the promotion code AI&Mathematics.
Preliminary program
Thursday 9 June
09:00 - 10:30 Session DIAMANT
10:30 - 10:45 Coffee break
10:45 - 12:15 Session STAR
12:15 - 13:30 Lunch break
13:30 - 15:00 Session GQT
15:30 - 17:00 Session NDNS+
17:00 - 18:30 Poster session + drinks
18:30 - 21:00 Workshop dinner
Friday 10 June
09:00 - 10:00 Session DIAMANT
10:00 - 10:15 Coffee break
10:15 - 11:15 Session STAR
11:15 - 12:15 Session GQT
12:15 - 13:30 Lunch break
13:30 - 14:30 Session NDNS+
14:30 - 17:00 Strategic session
17:00 - 18:00 Farewell drinks
Organizing team
- Christoph Brune (University of Twente), chair
- Leo van Iersel (TU Delft, co-chair), Mathias Staudigl (UM) and Steven Kelk (UM), representatives of DIAMANT
- Wil Schilders (TU/e) and Tristan van Leeuwen (CWI), representatives NDNS+
- Evgeny Verbitskiy, representative STAR
- Bram Mesland, representative GQT
- Olivia Muthsan (NWO)
Members
Associated Members
Publications
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Out, F, Cortés-Ortuño, D, Fabian, K, van Leeuwen, T, & de Groot, L.V. (2022). A first-order statistical exploration of the mathematical limits of Micromagnetic Tomography. Geochemistry, Geophysics, Geosystems, 23(4), e2021GC010184.1–e2021GC010184.18. doi:10.1029/2021GC010184
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Schut, D.E. (2022). Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory. doi:10.5281/zenodo.6351647
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Lucka, F, Pérez-Liva, M, Treeby, B.E, & Cox, B.T. (2022). High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion. Inverse Problems, 38(2), 025008.1–025008.39. doi:10.1088/1361-6420/ac3b64
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Longo, E, Alj, D, Batenburg, K.J, de la Rochefoucauld, O, Herzog, C, Greving, I, … Zeitoun, P. (2022). Flexible plenoptic X-ray microscopy. Photonics, 9(2), 98.1–98.13. doi:10.3390/photonics9020098
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Tataris, A, & van Leeuwen, T. (2022). A regularised total least squares approach for 1D inverse scattering. Mathematics, 10(2), 216.1–216.24. doi:10.3390/math10020216
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Bührer, M, Xu, H, Hendriksen, A.A, Büchi, F.N, Eller, J, Stampanoni, M, & Marone, F. (2021). Deep learning based classification of dynamic processes in time-resolved X-ray tomographic microscopy. Nature Scientific Reports, 11. doi:10.1038/s41598-021-03546-8
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Sero, D, Garachon, I, Hermens, E, van Liere, R, & Batenburg, K.J. (2021). The study of three-dimensional fingerprint recognition in cultural heritage: Trends and challenges. ACM Journal on Computing and Cultural Heritage, 14(4). doi:10.1145/3461341
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Bossema, F.G, Domínguez-Delmás, M, Palenstijn, W.J, Kostenko, A, Dorscheid, J, Coban, S.B, … Batenburg, K.J. (2021). A novel method for dendrochronology of large historical wooden objects using line trajectory X-ray tomography. Nature Scientific Reports, 11(1). doi:10.1038/s41598-021-90135-4
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Altantzis, T, Wang, D, Kadu, A.A, van Blaaderen, A, & Bals, S. (2021). Optimized 3D reconstruction of large, compact assemblies of metallic nanoparticles. Journal of Physical Chemistry C, 125(47), 26240–26246. doi:10.1021/acs.jpcc.1c08478
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Pilikos, G, de Korte, C.L, van Leeuwen, T, & Lucka, F. (2021). Single plane-wave imaging using physics-based deep learning. In IEEE International Ultrasonics Symposium. doi:10.1109/IUS52206.2021.9593589
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