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
EU Horizon 2020 grant award for realtime CT imaging project ‘xCting’
Project ‘xCTing’ from CWI’s Computational Imaging (CI) group and partners, has been awarded a grant from the EU Horizon 2020 Marie Curie Innovative Training Networks programme.
CWI develops New Techniques for Realtime Tomographic Reconstruction
JanWillem Buurlage of CWI's Computational Imaging group introduces various techniques that significantly reduce the time it takes to run conventional tomographic reconstruction algorithms without aﬀecting image quality. He will defend his thesis at 1 July.
Innovation fund North Holland supports CWI startup Photosynthetic B.V.
Photosynthetic B.V., a new CWI startup, has received a convertible loan of €300.000 from Innovation Fund North Holland. Photosynthetic is a deeptech startup working on a hardware solution for the microfabrication industry.
Can CT Scans Be Used to Quickly and Accurately Diagnose COVID19?
CWI’s Computational Imaging group joins research collaboration focusing on improving the accuracy of CT scan diagnosis
Current events
First annual meeting of the Dutch Inverse Problems Community
 20211125T00:00:00+01:00
 20211126T23:59:59+01:00
First annual meeting of the Dutch Inverse Problems Community
Start: 20211125 00:00:00+01:00 End: 20211126 23:59:59+01:00
The first annual meeting of the Dutch Inverse Problems Community will take place on 2526 November 2021 in conference center de Werelt, Lunteren.
Registration
The registration fee for this 2day event is EUR 235, which includes hotel and catering. Registering for a single day only is possible as well and costs EUR 50, which includes lunch.
You can register here. Currently, the registration is limited to 25 people and is on a firstcomefirstserved basis. If registration is full, we will put you on a waiting list and contact you if extra spots become available.
Preliminary program:
Thursday 25 November
09.30  10.00: Reception, coffee
10.00  12.30: Masterclasses 1 & 2 (in parallel)
12.30  14.00: Lunch break and poster session
14.00  16.30: Masterclasses 1 & 2 (in parallel)
16.30  17.30: Drinks and poster session
17:30  18:30: Brainstorm session on joint grant proposals etc.
18.30  : Dinner, followed by social program
Friday 26 November
09.00  09.30: Reception, coffee
09.30  10.30: Plenary talks
10.30  11.00: Coffee break
11.00  12.00: Plenary talks
12.00  13.30: Lunch break
13.30  14.30: Plenary talks
14:30  16:30: Panel discussion
Masterclasses
The masterclasses are meant to introduce the participants to a particular topic by a combination of lecture and handson exercises. Each masterclass lasts to whole day (10:00  16:30 with lunch break), so you can register for one masterclass. During registration you will be asked to pick a top 3 from the topics below. As we can only offer two masterclasses, you may not be assigned your first pick.
Basic inverse problems and imaging theory  Martin van Gijzen This master class will discuss the theory of linear discrete inverse problems with application to tomographic image reconstruction. Linear discrete inverse problems can be formulated as a leastsquares problem. This leastsquares problem is typically illposed, which means that it does not have a unique solution, or that the solution is very sensitive to noise. Consequently, we can not expect that a standard technique like solving an illposed leastsquares problem by solving the normal equations will lead to a relevant solution. However, the solution of such problems are of pivotal importance in many applications. The class starts with explaining how tomographic image reconstruction yields an illposed leastsquares problem. Then we review linear algebra techniques for solving such problems. We will introduce the concept of regularisation to make the problem wellposed, and discuss a number of ideas of how to regularise. Finally, we will present and study solution algorithms. The class finishes with an assignment in which the theory and algorithms are applied to a small but very illustrative tomographic reconstruction problem that models wave propagation in the earth crust. Entry requirements: Basic knowledge of linear algebra.
Optimisation techniques in inverse problems  Juan Peypouquet In this course, we will discuss how some optimisation techniques can be used to analyse and solve a class of inverse problems. The course will contain a selfcontained review of convex analysis, subdifferential calculus and optimality conditions, plus an introduction to iterative methods used to solve these kinds of problems. Entry requirements: Bachelorlevel knowledge of functional analysis and differential calculus is recommended
Data assimilation  Femke Vossepoel Data assimilation combines dynamic models with available observations to find the probability distribution of the model solution given the data. In the last decades, we see a growing application of data assimilation in the geosciences, but also in other fields, from economics to epidemiology. In this master class, we will explain the principles of data assimilation from a Bayesian perspective and provide a unified formulation of data assimilation that places various data assimilation methods and their applications in perspective. We will discuss how to use data assimilation for state and parameter estimation and we will discuss how these methods can deal with errors in the dynamic model and its control or forcing. Participants will experience the possibilities and limitations of data assimilation in an exercise with a toy problem. Entry requirements: MSclevel education in physics, maths, engineering or geosciences. Basic knowledge of inverse problem theory and Bayesian statistics.
Fundamentals of Acoustic and Electromagnetic Wave Field Theory  Koen van Dongen & Rob Remis Inverse wave field problems play an important role in many applications, such as reservoir imaging, hyperthermia cancer treatment, MRI, medical ultrasound, etc. Mathematical data and wave field models play a fundamental role in such problems. The models should capture the underlying physical processes as accurately as possible, of course, and efficient inverse solution techniques often exploit the structure and properties of these models. To effectively solve an inverse wave field problem, a proper understanding of the physical wave field processes is required. We therefore provide a crash course on acoustic and electromagnetic wave field theory. After this course, we hope that you have a better understanding of acoustic and electromagnetic wave propagation, Rayleigh integrals, Greens functions, scattering integrals, boundary conditions etc. Hopefully, this knowledge will help you to improve the mathematical model or solution method used to solve your inverse problem. Entry requirements: No specific requirements; basic knowledge of differential equations and/or physics is helpful.
PhD defense Rien Lagerwerf
 20211005T10:00:00+02:00
 20211005T11:00:00+02:00
PhD defense Rien Lagerwerf
Start: 20211005 10:00:00+02:00 End: 20211005 11:00:00+02:00
Everyone is invited to attend the public defense of Rien Lagerwerf of his PhD thesis:
Automatic and Efficient Tomographic Reconstruction algorithms
Promotor: Prof. dr. Joost Batenburg
Leiden Institute of Advanced Computer Science (LIACS), Universiteit Leiden
Copromotor: Dr. Willem Jan Palenstijn
Centrum Wiskunde & Informatica
Members
Associated Members
Publications

DomínguezDelmás, M, Bossema, F.G, Dorscheid, J, Coban, S.B, HallAquitania, M., Batenburg, K.J, & Hermens, E. (2021). Xray computed tomography for noninvasive dendrochronology reveals a concealed double panelling on a painting from Rubens’ studio. PLoS ONE, 16(8). doi:10.1371/journal.pone.0255792

Minnema, J, van Eijnatten, M.A.J.M, Der Sarkissian, H.H, Doyle, S, Koivisto, J, Wolff, J, … Batenburg, K.J. (2021). Efficient high coneangle artifact reduction in circular conebeam CT using deep learning with geometryaware dimension reduction. Physics in Medicine and Biology, 66(13). doi:10.1088/13616560/ac09a1

Andriiashen, V, van Liere, R, van Leeuwen, T, & Batenburg, K.J. (2021). Unsupervised foreign object detection based on dualenergy absorptiometry in the food industry. Journal of Imaging, 7(7). doi:10.3390/jimaging7070104

van Leeuwen, T, & Aravkin, A.Y. (2021). Variable projection for nonsmooth problems. SIAM Journal on Scientific Computing, S249–S268. doi:10.1137/20M1348650

Bossema, F.G. (2021). Three line trajectory Xray tomography datasets of a small wooden block. doi:10.5281/zenodo.4541555

Bossema, F.G. (2021). A CT dataset of a small wooden block. doi:10.5281/zenodo.4533882

Coban, S.B, Andriiashen, V, & Ganguly, P.S. (2020). Apple CT Data: Simulated parallelbeam tomographic datasets. doi:10.5281/zenodo.4212301

Javaherian, A, Lucka, F, & Cox, B.T. (2020). Refractioncorrected raybased inversion for threedimensional ultrasound tomography of the breast. Inverse Problems, 36(12). doi:10.1088/13616420/abc0fc

Viganò, N.R, Lucka, F, de la Rochefoucauld, O, Coban, S.B, van Liere, R, Fajardo, M, … Batenburg, K.J. (2020). Emulation of Xray lightfield cameras. Journal of Imaging, 6(12). doi:10.3390/jimaging6120138

Zeegers, M.T, Pelt, D.M, van Leeuwen, T, van Liere, R, & Batenburg, K.J. (2020). Taskdriven learned hyperspectral data reduction using endtoend supervised deep learning. Journal of Imaging, 6(12). doi:10.3390/jimaging6120132
Software
ASTRA Toolbox: Commercialclass software for tomography imaging
The ASTRA Toolbox is a MATLAB and Python platform providing scalable, highperformance GPU primitives for 2D and 3D tomography, including building blocks for advanced reconstruction algorithms.
RECAST3D: a realtime visualization platform for tomographic imaging
RECAST3D provides realtime tomographic reconstruction and visualization of arbitrarily oriented 2D slices in a 3D volume.
Current projects with external funding

Mathematics and Algorithms for 3D Imaging of Dynamic Processes ()

Nondestructive 3D spectral imaging: applications in the poultry industry ()

All in one cancer imaging optimisation using an integrated mathematical and deep learning (Cancer Imagiing)

Cervical Disease Maps

the Center for Optimal, RealTime Machine Studies of the Explosive Universe (CORTEX)

CT for Art: from Images to Patterns (IMPACT4Art)

MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING)

TranslationDriven Development of Deep Learning for Simultaneous Tomographic Image Reconstruction and Segmentation (None)

Deep learning and compressed sensing for ultrasonic nondestructive testin (PPS Applus RTD)

Universal ThreedimensiOnal Passport for process Individualization in Agriculture (UTOPIA)

Enabling Xray CT based Industry 4.0 process chains by training Next Generation research experts (xCTing)
Related partners

ABN AMRO Bank

Fraunhofer Gesellschaft

IBM

Katholieke Universiteit Nijmegen

Naturalis

Netherlands eScience Center

NIKHEF

Rijksmuseum Amsterdam

Universiteit Wageningen

University of Cambridge

Nederland Instituut voor Radio Astronomie

GREEFA

Katholieke Universiteit Leuven

RheinischWestfaelische Technische Hochschule Aachen

SURFsara B.V.

Universiteit Utrecht

Universiteit van Amsterdam