About us

Imaging at large-scale facilities offers unique opportunities for measuring a material’s microstructure. Most often, these measurements require quantitative image analysis to obtain the relevant information. To ensure a high scientific output from MAX IV, the QIM center aims at developing and using the most relevant tools for analyzing the data for a given problem.

3D Imaging is an extremely powerful technique to learn about the inside of materials, samples and specimens across a wide range of scientific disciplines, including industrial and clinical applications, as well as other uses. At the newly established and one of the most brilliant synchrotron sources in the world – the MAX IV synchrotron – five imaging beamlines are being planned. Each of these has a worldwide superior specification allowing for unprecedented spatial and temporal resolution across a series of different X-ray imaging modalities. These instruments will produce very large data in the form of sequences of 3D images. These data become useful only when it is turned into information by means of image quantification. This transformation calls for image analysts and expertise from the QIM center.

Our ambition is to bridge the gap between the researchers using the MAX IV and image analysis knowledge. We provide consultancy and collaboration in order to make the process of going from a scan to results as seamless and streamlined as possible. Collaborative efforts will be required to go from the scientific questions to the quantification of the data.

You can contact the QIM team by emailing info@qim.dk or approach one of the team members below.

Meet Our Team

The individuals associated with the QIM project are:

Anders Bjorholm Dahl
Anders Bjorholm DahlProfessor
Anders has been the head of section for the image analysis and computer graphics group at DTU since 2015. He finished his Ph.D. at DTU in 2009 and has been an employee of DTU since then. His research focuses on a variety of things related to image analysis, where his main focus has been on quantification of 3D volumetric data. (Google Scholar Profile)
Jon Sporring
Jon SporringProfessor
Jon Sporring received his Master and Ph.D. degree from the Department of Computer Science, University of Copenhagen, Denmark. In 2007-2012 and again since 2015 he is Vice-Chair for Research at Department of Computer Science, University of Copenhagen. His primary research field is Computer Science and particularly mathematical and medical image processing, computer graphics, information theory, and pattern recognition. To read more about his research, click here.
Rajmund Mokso
Rajmund MoksoSenior Scientist
Rajmund is a researcher and project manager at the MAX IV Laboratory (Lund University) and an honorary professor at the Technical University of Denmark. He is a specialist in X-ray physics with emphasis on the enhancement of X-ray imaging for biology and material science applications: development and applications of coherent imaging with improved temporal and spatial resolution. To read more about his research, click here.
Stephen Hall
Stephen HallAssociate Professor
Stephen is an associate professor at the Dept. of Solid Mechanics at the Faculty of Engineering (LTH) at Lund University. He is also in charge of the 4D-Imaging Lab x-ray tomography facility since 2011 after moving from Laboratoire 3R in Grenoble, France. To read more about his research, click here.
Vedrana Dahl
Vedrana DahlAssociate Professor
Vedrana Andersen Dahl received a Ph.D. degree in geometry processing in 2011. Her research interests revolve around geometric models for analysis of volumetric data. This includes volumetric segmentation, tomographic segmentation and methods based on deformable meshes. She developed image analysis tools with application in material science, industrial inspection and biomedicine. To read more about her research, click here.
Carsten GundlachSenior Research Officer
Coming soon
Anders Nymark Christensen
Anders Nymark ChristensenAssociate Professor
Anders has worked with 3D image analysis since 2010. First in a biological setting, and has since broadened his scope to geological and industrial materials. His research focuses are multivariate statistics, 3D-segmentation, and quantification – using a variety of techniques. He is currently employed as an assistant professor at DTU Compute.
Francois Bernard Lauze
Francois Bernard LauzeAssociate Professor
François is associate professor at the Department of Computer Science, University of Copenhagen. He received a PhD degree in Algebraic Geometry from the University of Nice – Sophia Antipolis and a PhD degree in Image Analysis for the IT University in Copenhagen in 2004. His primary research interests are Mathematics of Image processing, especially related to Geometry and Inverse Problems. Link
Behnaz Pirzamanbein
Behnaz PirzamanbeinPostdoctoral Researcher
Behnaz has a Ph.D. in mathematical statistics and two master’s degrees in mathematics and modeling, and mathematical statistics. During her master’s theses, she worked with image processing methods both theoretically and applied. She has been a PostDoc at DTU compute since 2017, working with change detection methods and software development for satellite images. Her research interest is in statistical methods and statistical properties of images in space and time. To read more about Behnaz, click here
Monica Jane Emerson
Monica Jane EmersonPostdoctoral Researcher
Monica has a Ph.D. in statistical image analysis and a Masters in telecommunications engineering. She has worked in the intersection of materials science, X-ray CT imaging and statistical image analysis. Her research interest lies in the application of 3D segmentation and quantification techniques, which she enjoys developing in close collaboration with academic and industrial researchers from other scientific disciplines. (Google Scholar Profile)
Silas Nyboe Ørting
Silas Nyboe ØrtingPostdoctoral Researcher
Silas is a Post Doc researcher affiliated with the Center for Quantification of Imaging Data from Max IV (QIM).

Silas received a Ph.D degree in medical image analysis in 2019 and a Master’s degree in computer sicence in 2016, both from the Department of Computer Science, University of Copenhagen. His main area of research is within biomedical image analysis with an emphasis on the practical application of machine learning in settings where labeled data are scarce, expensive and noisy. This includes weakly supervised machine learning, as well as image annotation strategies such as crowdsourcing, visual similarity and low-effort annotations.

Hans Martin Kjær
Hans Martin KjærResearcher
With a background in biomedical engineering and a PhD in image analysis, Martins primary interest is the development of practical methods and pipelines for efficient 3D data analysis of biological samples. Proficient with a wide array of tools from the image analysis toolbox, he is geared to enable researchers and collaborators to pry out the quantitative information hidden within their data.
Johan Hektor
Johan HektorPostdoctoral Researcher
Johan is working as an applications expert in image analysis at LUNARC, the centre for scientific and technical computing at Lund University. He has been a postdoc at the P21.2 beamline at the PETRA III synchrotron, where he worked mainly on data analysis workflows for diffraction based imaging experiments. Johan has a Ph.D. in solid mechanics from Lund University. To read more about his research click here
Jakob Sauer Jørgensen
Jakob Sauer JørgensenResearcher
Jakob received his PhD in Applied Mathematics from DTU in 2013 on the topic of sparsity-regularised reconstruction algorithms for computed tomography with noisy or incomplete data. After a postdoc at DTU, he moved to The University of Manchester, UK in 2015 where he developed reconstruction algorithms and software in the Manchester X-ray Imaging Facility. In 2018 he secured a Manchester Presidential Fellowship (tenure track) to work on hyperspectral tomography. In April 2020 he re-joined DTU as a Researcher affiliated with the Villum Investigator Project on Computational Uncertainty Quantification for Inverse Problems (CUQI). Google Scholar Profile
Chenhao Wang
Chenhao WangResearch Assistant
Chenhao received his Bachelor’s degree in Science & IT in 2017, and Master’s degree in Bioinformatics in 2019, both from University of Copenhagen. He is interested in general scientific image analysis and developing the supporting software tools. He is based at the IMAGE section of DIKU.
William Laprade
William LapradeResearch Assistant
William holds a bachelor’s degree in mathematics and computer science from Westfield State University and a master’s degree in computer science from the University of Copenhagen. He is interested in machine learning and working with medical imaging data in developing software and algorithms that help to advance research and solve problems in the medical fields. Currently, he is working as a research assistant in the image section at DIKU.
Yuan Wang
Yuan WangPhD Student
M.S. : computer science and technology, Xidian University
Research Interests: deep learning, intelligent optimization algorithm, computer vision
Present Condition: Phd student of Professor Jon, involving in the project C-arm