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
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.
Quality guarantee for tomographic images developed
CWI researcher Wagner Fortes has developed new methods to determine the quality of tomographic images. Tomography is an imaging technique used in for instance medical CT scanners and electron microscopes that uses penetrating beams to record a series of two-dimensional images from a range of angles.
CWI leads European network of imaging specialists
Centrum Wiskunde & Informatica (CWI) will lead the research network EXTREMA, which was officially launched yesterday with a kick-off meeting in Brussels. EXTREMA aims to create an interdisciplinary research community consisting of both X-ray specialists and mathematicians. Together they will work on the next generation of imaging techniques to advance both science and industrial R&D.
Current events
MUMMERING 3rd General Assembly
- 2019-09-13T09:00:00+02:00
- 2019-09-13T15:00:00+02:00
MUMMERING 3rd General Assembly
Start: 2019-09-13 09:00:00+02:00 End: 2019-09-13 15:00:00+02:00
Location workshop: Academiegebouw, Universiteit Leiden.
The General Assembly will be preceded by the MUMMERING Supervisory Board Meeting, which will take place on Thursday 12 September in the afternoon from 4pm to 6pm.
The registration fee of 50 euro includes the dinner on Thursday evening as well as lunch and refreshments on Friday. Please register by using the electronic conference registration form here.
Program
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09:00 |
Welcome |
Sara Bals, Hermen Jan Hupkes Jens Wenzel Andreasen |
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Info: MSCA evaluation questionnaire, see enclosure |
Hanne |
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09:10 |
MUMMERING’s research objective and work packages | Jens Wenzel Andreasen |
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MUMMERING Communication Plan |
Azat Slyamov, Ja-Keoung Koo, Elise Otterlei Brenne |
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| 09:30 |
ESR presentation, team 1 |
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10:00 |
ESR presentation, team 2 |
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10:30 |
Break |
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11:00 |
ESR presentation, team 3 |
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11:30 |
ESR presentation, team 4 |
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12:00 |
Lunch |
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13:00 |
ESR presentation, team 5 |
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13:30 |
ESR presentation, team 6 |
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14:00 |
ESR presentation, team 7 |
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14:30 |
Summing up and conclusion |
Jens Wenzel Andreasen |
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Next meetings: Winter school , 4th workshop and 4th General Assembly, Bucharest Feb. 2020 |
Mihai Iovea |
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15:00 |
Closing |
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- Email: Hermen Jan Hupkes
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iCal
Mummering Workshop on Dynamic Imaging
- 2019-09-10T14:00:00+02:00
- 2019-09-12T12:30:00+02:00
Mummering Workshop on Dynamic Imaging
Start: 2019-09-10 14:00:00+02:00 End: 2019-09-12 12:30:00+02:00
Mummering Workshop on Dynamic Imaging, 10-12 September 2019, University Leiden
Online registration is now open for MUMMERING PhD students.
The registration fee is € 450 and covers a social event, lunch and refreshments. You can register by using the electronic conference registration form here. If you are not a MUMMERING student and would like to participate, please contact Felix Lucka to enquire about the possibilities.
Location workshop: Academiegebouw, Universiteit Leiden
Hotels:
Best Western Leiden
Ibis Leiden Centre
Tulip Inn Leiden Centre
Fletcher Wellness-Hotel Leiden
Workshop description:
The basis of understanding what happens in a physical process is often to see it in action, in 3D. Dynamic tomographic imaging is therefore of high interest in various scientific, industrial and clinical applications. However, while obtaining high quality 3D images of static objects is feasible with many tomography modalities nowadays, imaging fast dynamic processes with both high spatial and temporal resolution remains very challenging from a mathematical, computational and experimental perspective. In this workshop, leading researchers will discuss these challenges and illustrate recent advances for different imaging modalities.
Learning objectives:
- Mathematical modeling of dynamic imaging applications
- Dynamic image reconstruction approaches and algorithms
- Computational realization and challenges
Program for day 1: Tuesday September 10th
14:00 – 14:30 Registration
14:30 – 15:30 Lecture: TBA
15:30 – 16:00 Coffee break
16:00 – 17:00 Lecture: Wiebke Albrecht (University of Antwerp)
Program for day 2: Wednesday September 11th
10:00 – 11:00 Lecture: Angelica I. Aviles-Rivero (University of Cambridge)
11:00 – 11:30 Coffee break
11:30 – 12:30 Lecture: Martin Burger (FAU Erlangen-Nürnberg)
14:30 – 17:00 Collaboration session
19:00 Social event (to be announced)
Program for day 3: Thursday Sebtember 12th
10:00 – 11:00 Lecture: Federica Marone (PSI)
11:00 – 11:30 Coffee break
11:30 – 12:30 Lecture: Timothy Burnett (University of Manchester, tentative)
- Email: Hermen Jan Hupkes
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iCal
Members
Associated Members
Publications
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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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Zelflerende machines en jagen op kosmische explosies en versnellen innovatie - Astroblogs. (2019). Zelflerende machines en jagen op kosmische explosies en versnellen innovatie - Astroblogs.
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Zelflerende neurale netwerken gaan kosmische radioflitsen waarnemen - Tweakers. (2019). Zelflerende neurale netwerken gaan kosmische radioflitsen waarnemen - Tweakers.
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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
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Buurlage, J, Bannink, T.R, & Bisseling, R.H. (2018). Bulk: A modern C++ interface for bulk-synchronous parallel programs. In Euro-Par 2018: Parallel Processing (pp. 519–532). doi:10.1007/978-3-319-96983-1_37
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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Rapid 3D Printing through full-field volumetric photolithography ()
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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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Photosynthetic B.V.
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University College London