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

Making the invisible visible

Making the invisible visible

CT machines are becoming the standard tool for looking inside objects of all kinds in research and industry. The FleX-ray Lab at CWI is making this type of imaging more accessible to math and computer science researchers. It's also drawing interest from the art, history, and the social sciences community.

Making the invisible visible - Read More…

Making the invisible visible

Making the invisible visible

CT machines are becoming the standard tool for looking inside objects of all kinds in research and industry. The FleX-ray Lab at CWI is making this type of imaging more accessible to math and computer science researchers. It's also drawing interest from the art, history, and the social sciences community.

Making the invisible visible - Read More…

Current events

First annual meeting of the Dutch Inverse Problems Community

  • 2021-11-25T00:00:00+01:00
  • 2021-11-26T23:59:59+01:00
November 25 Thursday

Start: 2021-11-25 00:00:00+01:00 End: 2021-11-26 23:59:59+01:00

Conference center de Werelt, Lunteren

The first annual meeting of the Dutch Inverse Problems Community will take place on 25-26 November 2021 in conference center de Werelt, Lunteren.


 Registration

Registration is currently closed because we unfortunately cannot accommodate any more participants in the current venue. For questions about existing registrations, please contact t.van.leeuwen@cwi.nl.

 


 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: Hai Xiang Lin
                         Rikkert Hindriks: Reconstruction of functional brain networks from EEG/MEG sensor data. 

10.30 - 11.00: Coffee break

11.00 - 12.00: Maureen van Eijnatten: Deep learning for image-guided treatments
                         Jeannot Trampert: 
Estimating the posterior using prior sampling: The case of rapid seismic source inference.

12.00 - 13.30: Lunch break

13.30 - 14.30: Harry van Zanten: Bayesian inverse problems
                         Jeroen Kalkman: Imaging challenges in optical tomography.

14:30 - 16:30: Panel discussion


 Masterclasses

1. 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 self-contained review of convex analysis, subdifferential calculus and optimality conditions, plus an introduction to iterative methods used to solve these kinds of problems. Entry requirements: Bachelor-level knowledge of functional analysis and differential calculus is recommended

2. 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: MSc-level education in physics, maths, engineering or geosciences. Basic knowledge of inverse problem theory and Bayesian statistics.

 

Lectures

Rikkert Hindriks - Reconstruction of functional brain networks from EEG/MEG sensor data. Electroencephalography (EEG) and magnetoencephalography (MEG), respectively, record scalp potentials and magnetic fluxes surrounding the head that are induced by electrical currents inside the brain. I will discuss the biophysical principles underlying such measurements and provide an overview of the inverse methods that are used to reconstruct brain activity from sensor EEG/MEG data. I will focus on the reconstruction of functional brain networks, which is currently one of the most activity areas of research in this field. 

Maureen van Eijnatten - Deep learning for image-guided treatments. Deep learning has enormous potential to automate and augment various image-guided treatments, thereby opening completely new avenues to personalize patient care. However, there are still some challenges to overcome when applying state-of-the-art deep neural networks to medical images. Training such networks, for example, requires vast amounts of (imaging) data including accurate ground truth labels that typically need to be manually created, whereas increasingly strict privacy regulations prohibit sharing patient data between institutions. Furthermore, existing deep learning methods often do not generalize well to variations in imaging protocols, scanner manufacturers and patient populations. This lecture will highlight some of the main opportunities and challenges of deep learning in the areas of maxillofacial surgery, oncologic surgery and radiotherapy.

Jeannot Trampert - Estimating the posterior using prior sampling: The case of rapid seismic source inference. Suppose that we have a non-linear problem d=g(m), where d is a data vector, m the model vector to be inferred and g the mapping. In the Bayesian framework, the problem consists of estimating the probability density function p(d|m). In the absence of a closed form answer, the problem can be solved by sampling. I will show that this sampling can be approached in two ways, by posterior sampling (e.g. MCMC) or prior sampling. The latter can be numerically advantageous when an inference problem must be solved fast or repeatedly. The main difference between the two sampling strategies is at what stage you introduce the actual measurements. Just as there are many algorithms for posterior sampling, the inference based on prior samples can be done in various ways. We chose mixture density networks (MDNs). I will quickly explain the principle before moving to a toy example illustrating the main points. I will then show a real example for the rapid inference of moment tensors in the Californian region.

Refs: 
Kaeufl P., Valentine A.P., de Wit R.W.L., Trampert J., 2016. Solving probabilistic inverse problems rapidly with prior samples, Geophys. J. Int., 205, 1710–1728.
Kaeufl P., Valentine A.P., Trampert J., 2016. Probabilistic point source inversion of strong-motion data in 3-D media using pattern recognition—a case study for the 2008 Mw 5.4 Chino Hills earthquake, Geophys. Res. Lett., 43, doi:10.1002/2016GL069887.

Harry van Zanten - Bayesian inverse problems. In recent years the Bayesian approach to solving inverse problems has become quite popular. In this talk I intend to give a brief overview of some of the mathematical work that has been done on the behavior and performance of these methods.

Jeroen Kalkman - Imaging challenges in optical tomography. In optical tomography light does not behave like straight pencil-like rays that X-rays do in X-ray computed tomography. Light propagation is strongly influence by diffraction, refraction, and scattering. When not properly taken into account in the system design and reconstruction algorithms these effects can severely affect the image quality in optical tomography. In this presentation I will show how some of these effects can be mitigated in hardware by applying optical sample clearing or physical measurement principles such as coherent and confocal gating. I will show how the effects of diffraction on the reconstructed image have been addressed through analytical theory of the optical point spread function combined with 2D image deconvolution in the reconstructed image. In addition, we have dealt with these effects through iterative reconstruction of the inverse problem discretising the object and the imaging geometry. We have solved the inverse problem in 2D, however challenges remain to solve the optical tomography diffraction in 3D for an extremely large data volume (>109 voxels).

 

 

Members

Associated Members

Publications

Software

Current projects with external funding

  • Mathematics and Algorithms for 3D Imaging of Dynamic Processes ()
  • Non-destructive 3D spectral imaging: applications in the poultry industry ()
  • Cambridge RG99590 AIO cancer imaging optimisation (Cancer Imaging Optimisation)
  • Cervical Disease Maps
  • the Center for Optimal, Real-Time Machine Studies of the Explosive Universe (CORTEX)
  • CT for Art: from Images to Patterns (IMPACT4Art)
  • MUltiscale, Multimodal and Multidimensional imaging for EngineeRING (MUMMERING)
  • Translation-Driven 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 Three-dimensiOnal Passport for process Individualization in Agriculture (UTOPIA)
  • Enabling X-ray 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
  • Rheinisch-Westfaelische Technische Hochschule Aachen
  • SURFsara B.V.
  • Universiteit Utrecht
  • Universiteit van Amsterdam