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
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.
CWI part of NWA CORTEX consortium
The National Science Agenda has awarded a 5 million euro grant to CORTEX – the Center for Optimal, Real-Time Machine Studies of the Explosive Universe. Self-learning machines will hunt for explosions in the universe and speed up innovations in industry and society.
Veni grant for Daniël Pelt
The Netherlands Organisation for Scientific Research (NWO) has awarded a Veni grant to CWI researcher Daniël Pelt.
Computational Imaging group and Rijksmuseum project awarded with NICAS funding
The Netherlands Institute for Conservation, Art and Science (NICAS) has announced which proposals have been awarded in the call "An Integrated Approach to the Scientific Study and Conservation of Art", organised by NWO.
Members
Associated Members
Publications
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Der Sarkissian, H.H, Lucka, F, van Eijnatten, M.A.J.M, Colacicco, G, Coban, S.B, & Batenburg, K.J. (2019). A cone-beam X-ray computed tomography data collection designed for machine learning. Scientific Data, 6(1). doi:10.1038/s41597-019-0235-y
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Colman, K.L, de Boer, H.H, Dobbe, J.G.G, Liberton, N.P.T.J, Stull, K.E, van Eijnatten, M.A.J.M, … van der Merwe, A.E. (2019). Virtual forensic anthropology: The accuracy of osteometric analysis of 3D bone models derived from clinical computed tomography (CT) scans. Forensic Science International, 304. doi:10.1016/j.forsciint.2019.109963
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Sero, D, Zaidi, A, Li, J, White, J.D, González Zarzar, T.B.G, Marazita, M.L, … Claes, P. (2019). Facial recognition from DNA using face-to-DNA classifiers. Nature Communications, 10(1). doi:10.1038/s41467-019-10617-y
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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 (Switzerland), 9(12). doi:10.3390/app9122445
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Bossema, F.G, Burger, J.P, Bratton, L, Challenger, A, Adams, R.C, Sumner, P, … Smeets, I. (2019). Expert quotes and exaggeration in health news: A retrospective quantitative content analysis. Wellcome Open Research. doi:10.12688/wellcomeopenres.15147.2
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Viganò, N.R, Martínez Gil, P.M, Herzog, C, de la Rochefoucauld, O, Liere, R.V. (Robert Van), & Batenburg, K.J. (2019). Advanced light-field refocusing through tomographic modeling of the photographed scene. Optics Express, 27(6), 7834–7856. doi:10.1364/OE.27.007834
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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), 128–128. doi:10.3390/jimaging4110128
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Tick, J, Pulkkinen, A, Lucka, F, Ellwood, R, Cox, B.T, Kaipio, J.P. (Jari), … Tarvainen, T. (2018). Three dimensional photoacoustic tomography in Bayesian framework. The Journal of the Acoustical Society of America, 144(4), 2061–2071. doi:10.1121/1.5057109
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Der Sarkissian, H.H, Viganò, N.R, & Batenburg, K.J. (2018). A data consistent variational segmentation approach suitable for real-time tomography. Fundamenta Informaticae, 163(1), 1–20. doi:10.3233/FI-2018-1729
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Hauptmann, A, Arridge, S, Lucka, F, Muthurangu, V, & Steeden, J.A. (2018). Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning–proof of concept in congenital heart disease. Magnetic Resonance in Medicine. doi:10.1002/mrm.27480
Software
Current projects with external funding
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Machine learning for large 3D tomographic images ()
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Non-destructive 3D spectral imaging: applications in the poultry industry ()
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Real-Time 3D Tomography ()
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Mathematics and Algorithms for 3D Imaging of Dynamic Processes
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PPS contract Planmeca
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CT for Art: from Images to Patterns (IMPACT4Art)
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Combining cycloidal computed tomography with machine learning: a mechanism to disrupt the costly relationship between spatial resolution and radiation dose (ML-CYCLO-CT)
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MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING)
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Deep learning and compressed sensing for ultrasonic nondestructive testin (PPS Applus RTD)
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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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Naturalis
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Planmeca
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Rijksmuseum Amsterdam
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Universidad Politécnica de Madrid
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University College London