I am an applied mathematician and assistant professor for computational and mathematical methods for imaging at the University of Hamburg. Therefore, I am member of Center of Optimization and Approximation and associated with the Institute for Biomedical Imaging at the University hospital Hamburg Eppendorf (UKE).

My actual research covers large-scale inverse problems in physical and medical applications such as for example X-ray scattering, photo acoustic tomography and magnetic particle imaging. More precisely, I am working on variational regularization methods, dynamic inverse problems, Bayesian inversion, and approximation error modelling.

- Stephanie Blanke
- Stasis Chuchurka
- Dr. Tram Do
- Dr. Milain Kas
- Ankita Negi
- Christiane Schmidt
- Lena Zdun

Master students

- Anton Jabs
- Felix Kieckhäfer
- Alban Kreyziu

- Hendrik Baden
- My Linh Bui
- Amol Bains
- Finn Hansen
- Jonas Marquardt

I am looking for a PhD candidate within the graduate school (project S3). The PhD topic will belong to the field of inverse problems, modelling and approximation with application to magnetic particle imaging. For more details, see the job call

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doi:10.1007/s10851-018-0807-z.

Erb, W.; Weinmann, A.; Ahlborg, M.; Brandt, C.; Bringout, G.; Buzug, T.M.; Frikel, J.; Kaethner, C.; Knopp, T.; März, T.; Möddel, M.; Storath, M.; Weber, A.: Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging. Inverse Problems, 34(5):2018.

doi:10.1088/1361-6420/aab8d1

Bathke, C.; Kluth, T.; Brandt, C. & Maaß, P.: Improved image reconstruction in magnetic particle imaging using structural a priori information. International Journal On Magnetic Particle Imaging, 3(1):2017.

online availble here

Storath, M.; Brandt, C.; Hofmann, M.; Knopp, T.; Salamon, J.; Weber, A. & Weinmann, A.: Edge preserving and noise reducing reconstruction for magnetic particle imaging. IEEE Transactions on Medical Imaging, 36(1):74-85, 2017.

doi:10.1109/TMI.2016.2593954

Brandt, C., Maass, P., Piotrowska-Kurczewski, I., Schiffler, S., Riemer,O., and Brinksmeier, E.: Mathematical methods for optimizing high precision cutting operations. International Journal of Nanomanufacturing, 8(4):306–325, 2012.

doi:10.1504/IJNM.2012.048580.

Brandt, C., Niebsch, J., Ramlau, R., and Maass, P.: Modeling the influence of unbalances for ultra-precision cutting processes. ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 91(10):795–808, 2011.

doi:10.1002/zamm.201000155.

Piotrowska, I., Brandt, C., Karimi, H. R., and Maass, P.: Mathematical model of micro turning process The International Journal of Advanced Manufacturing Technology, Volume 45(1):33–40, 2009.

doi:10.1007/s00170-009-1932-z.

Brandt,C., Niebsch, J., and Vehmeyer, J.: Modelling of ultra-precision turning process in consideration of unbalances. Advanced Materials Research, 223:839–848, 2011.

doi:10.4028/www.scientific.net/AMR.223.839

Gossiaux, P.B., Aichelin, J., Brandt, C., Gousset, T., and Peigné, S.: Energy loss of a heavy quark produced in a finite-size quark-gluon plasma. Journal of Physics G: Nuclear and Particle Physics, 34(8):S817, 2007.

doi:10.1088/0954-3899/34/8/S103.

Gossiaux, P.B., Peigné, S., Brandt, C., and Aichelin, J.: Energy loss of a heavy quark produced in a finite size medium. Journal of High Energy Physics, 4(04):012, 2007.

doi:10.1088/1126-6708/2007/04/012.

available by Logos

doi:DOI:10.1007/978-3-642-32448-2.

Brandt, C., Krause, A., Niebsch, J., Vehmeyer, J., Brinksmeier, E., Maass, P., and Riemer, O.: Process Machine Interactions, Surface Generation Process with Consideration ot the Balancing State in Diamond Machining. Lectures Notes in Production Engineering. Springer, 2013.

doi:DOI: 10.1007/978-3-642-32448-2.

Piotrowska-Kurczewski, I., Brandt, C., Maass, P., and Riemer, O.: Micro Metal Forming, chapter Simulation technologies - Inverse Modelling, pages 368–379. Springer, 2013.

Brandt, C., Piotrowska, I., Karimi, H., Niebsch, J., Ramlau, R., Krause, A., Riemer, O., and Maass, P.: Process machine interaction model for turning processes. International Journal of Control Theory and Applications, 1(2):145–153, 2008.

Kluth, T.; Hahn, B.N. and Brandt, C.: Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging. Proceedings of the International Workshop on Magnetic Particle Imaging 2019, March 17-19. 2016, New York, USA, 2019.

Brandt, C. and Hauptmann, A.: Fast temporal regularized reconstructions for magnetic particle imaging. Proceedings of the International Workshop on Magnetic Particle Imaging 2019, March 17-19. 2016, New York, USA, 2019.

Storath, M., Brandt, C., Hofmann, M., Knopp, T. , Weber, A., Weinmann, A.: Fused lasso regularization for magnetic particle imaging. Proceedings of the International Workshop on Magnetic Particle Imaging 2016, March 16-18. 2016, Lübeck, Germany, 2016.

Brandt, C., Niebsch, J., and Vehmeyer, J.: Modelling of ultra-precision turning process in consideration of unbalances. In Outeiro, J.C., editor, Proceedings of the 13th CIRP Conference on Modeling of Machining Operations, May 12-13. 2011, Sintra, Portugal, volume 223, pages 839–848. 2011. www.scientific.net/AMR.223.839>

Niebsch, J., Ramlau, R., and Brandt, C.: On the interaction of unbalances and surface quality in ultra-precision cutting machinery. In SIRM 2011, Darmstadt, Germany. 2011.

Brandt, C., Niebsch, J., Maass, P., and Ramlau, R.: Simulation of process machine interaction for ultra precision turning. In Proceedings of the 2nd International Conference on Process Machine Interactions, June 10-11, 2010, Vancouver, Canada. 2010.

Brandt, C., Krause, A., Brinksmeier, E., and Maass, P.: Force modelling in diamond machining with regard to the surface generation process. In Proceedings of the 9th International Conference and Exhibition on Laser Metrology, machine tool, CMM and robotic performance, LAMDAMAP 2009, London, 30.06.-02.07.2009, pages 377–386. 2009.

Gossiaux, P.B., Peigné, S., Brandt, C., and Aichelin, J.: Energy loss of a heavy quark produced in a finite-size quark-gluon plasma

doi:10.1088/0954-3899/34/8/S103. QM2006 Proceedings; 19th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions (QM2006), Shanghai, China, November 14-20, 2006.

International workshop on magnetic particle imaging 2020 7.-9.9.2020

Online conference

SIAM conference on imaging, 6.-9.7.2020

Online conference

Invited by Gael Rigaud and Fatma Terzioglu

Synergistic Reconstruction Symposium, 3.-6. November 2019

Manchester, Great Britain

Invited by Dr. Jacob Jorgensen

International congress on industrial and applied mathematics (ICIAM 2019), 15.-19.7.2019

Valencia, Spain

Invited by Kristian Bredies & Antonin Chambolle

International conference on applied inverse problems (AIP 2019), 8.-12.7.2019

Grenoble, France

Invited by Felix Lucka, Tatiana Bubba, Sophia Coban & Samuli Siltanen

International workshop on magnetic particle imaging 2019 17.-19.3.2019

New York, USA

SIAM conference on computational science and engineering, 25.2-1.3.2019

Spokane, USA

Invited by Samuli Siltanen and Tatiana Bubba

Tomographic inverse problems: theory and application

Oberwolfach 2019, 27.1-2.2.2019

EUCCO 2018, 10.-12.6.2018

Trier, Germany

Plenary talk

Siam Imaging 2018, 5.-8.6.2018

Bologna, Italy

Invited by Traufiquar Khan and Cristiana Sebu

GAMM, March 2018, 19.-23.3.2018

München, Germany

Invited by Felix Krahmer and Benedict Wirth

Conference on Applied Inverse Problems, May 2017

Hangzhou, China

Imaging with modulated/incomplete data, 22.-24.9.2016

Graz, Austria

Inverse problems seminar at Helsinki University, 15.6.2016

Helsinki, Finland

Invited by Samuli Siltanen

SIAM Imaging 2016

23.-26.5.2016, Albuquerque, USA

Invited by Cecile Louchet

International Workshop on Magnetic Particle Imaging IWMPI 2016

16.-18.3.2016, Lübeck, Germany

Inverse Days 2015, 8.-10.12.2015

Lappeenranta, Finland

Mini-symposium on "Mathematical Methods for MPI" at the DMV 2015, 21.-25.9.2015,Hamburg

Invited by Wolfgang Erb

Applied Inverse Problems 2015

May 2015, Helsinki, Finland

Seminar AG Imaging, University Münster,

3.12.2014

Invited by Martin Burger

DSR seminar at the Helmholtz Institut München

7.7.2014

Invited by Frank Filbir

** Sparse Image Reconstruction for Photoacoustic Tomography Using Shearlets **

Workshop "Wavelets and Applications"

22.5.2014, Université Libre de Bruxelles, Belgium

Invited by Christine de Mol

SIAM Conference on Imaging Science

12-14.5.2014, Hongkong, China

Mecklenburger Workshop "Approximationsmethoden und schnelle Algorithmen, 17-20.03.2014, Hasenwinkel

Advances In Mathematical Image Processing, 30.09.-2.10.2013, Annweiler

Applied Inverse Problems Conference AIP 2013, 1.-5.07.2013, Daejeon, Korea

Inverse Problems: Modelling and Simulation 2012, 21.-26.05.2012, Antalya, Turkey

Oberseminar Analysis Universität Osnabrück, 14.05.2013, Osnabrück

(invited talk)

Winterseminar der AG Technomathematik der Universität Bremen, 2012, 28.-29.2.2912, Ganderkesee

Magnetic Particle Imaging (MPI) is a novel imaging modality for biomedical diagnostics that determines the spatial distribution of magnetic nanoparticles by measuring the non-linear magnetization response of the particles to an applied magnetic field. The advantages of MPI are the high dynamic spatial and temporal resolution and that it does not employ any ionizing radiation.

** Flow estimation with a priori edge information for MPI/MRI data **, joint work with Tobias Kluth

**Joint reconstruction in multi-color MPI**, joint work with Martin Hofmann and Tobias Knopp

**Approximation error modelling for model-based MPI**, joint work with Aku Seppänen

**Time based regularization for 4D MPI**, joint work Andreas Hauptmann

**Reconstruction methods for dynamic inverse problems **, PhD project of Christiane Schmidt

**Blood flow modeling and estimation by magnetic particle imaging **, PhD project of Stephanie Blanke

It is a joint research within of the scientific network "Mathematical methods for Magnetic Particle Imaging (MIEM). The network funded by the DFG consists of 9 researchers and it is lead by Dr. Wolfgang Erb from the University of Lübeck. For more information, see the official MIEM homepage.

The main objective of this research is the development of mathematical methods for three imaging modalities in biological and medical applications: fluorescence microscopy (FM), photo-acoustic tomography (PAT) and magnetic particle imaging (MPI). All imaging applications have in common that the desired object such as in PAT and MPI the cancer cell in the human body is not directly observable but can be measured only indirectly.

In order to reconstruct the desired object, one has to solve the so-called inverse problem. In this project, mathematical methods for solving inverse problems are developed which consider modelling errors and uncertainties of the underlying models in the inversion. It is expected that the image reconstruction quality will be increased significantly in all three applications. In particular, the improvements are assumed to contribute to the development of PAT and MPI as medical imaging techniques in clinical practice.

This project is founded by the Academy of Finland.

Related Publications:

Brandt, C.; Seppänen, A. : Recovery from errors due to domain truncation in magnetic particle imaging -- approximation error modeling approach. Journal of Mathematical Imaging and Vision, 2018.10.1007/s10851-018-0807-z

Novel X-ray sources (in particular FELs) allow for the first time to study complex chemical reactions and catalysis in time-dependent manner. While x-ray diffraction gives access to the structure of the underlying system (atomic position), x-ray emission spectroscopy is a complementary technique, to gain chemical knowledge (such as, for example the spin and oxidation state of a particular reaction center). The time-dependent observation of the breaking and formation of electronic bonds is, however, still a delicate task and relies on the interpretation of experimental data based on models.

At the current stage, a few pioneering groups perform x-ray scattering and x-ray emission spectroscopy within one experimental set up and build models to interpret these data. The data analysis and the reconstruction of the underlying physical quantities (determination of the electron density by solving the phase problem of crystallography) are, however, performed independently and separately for each method. In this project, we aim for the next rigorous step, by developing a common data analysis, reconstruction and model building approach combining x-ray diffraction and emission spectroscopy. This novel holistic approach has the potential to strongly improve on the resolution of the chemically-relevant valence electronic density and their current densities.

This project is founded by the DESY Strategy Fund. It is a joint project with Prof. N. Rohringer and Prof. S. Techert from DESY.

The proposed interdisciplinary project between the natural and mathematical/information sciences is understood as a kick-of project to develop novel x-ray data analysis methods for combined x-ray diraction and x-ray emission spectroscopy measurements at high-brilliance x-ray sources. While coherent x-ray diraction gives access to the structure (position of atoms) of a chemical compound or reaction unit, x-ray emission spectroscopy is a complementary and sensitive probe to the changes of the chemical bonds. The interpretation of the latter strongly relies on comparison with theoretically calculated spectra. Pioneering experiments at storage-ring based x-ray sources and x-ray free electron lasers (XFELs) have shown the possibility for combining x-ray diraction, x-ray scattering and x-ray emission spectroscopy in one high-quality experiment. Currently, the data analysis and reconstruction of the underlying physical quantities of interest (electron density, oxidation state, electron density, etc.) are undertaken independently from the two techniques. Here, we aim for a common data analysis and density reconstruction method, based on an underlying common electronic structre (quantum chemistry) model. Experimental x-ray diffraction and x-ray emission data will be directly implemented as constraints in the minimization approaches of ab-initio quantum chemistry methods, in the spirit of quantum crystallography. In this way chemical and structural information can be reconstructed in a coherent way.

This project is founded by the BMBF. It is a joint project with Prof. N. Rohringer from DESY/Department of physics(UHH).

Nawwas, L., Brandt, C., Szwargulski, P., Knopp, T., and Möddel, M.:
Reduction of Bias for Sparsity Promoting Regularization in MPI
, submitted to Interational Journal of Magnetic particle imaging.

Brandt, C. and Hauptmann, A.: Real time reconstruction for 4D Magnetic Particle Imaging.

Brandt, C. and Schmidt, C.: Modeling magnetic particle imaging for dynamic tracer concentrations , submitted to Journal of Sensing and Imaging

Brandt, C. and Hauptmann, A.: Real time reconstruction for 4D Magnetic Particle Imaging.

Brandt, C. and Schmidt, C.: Modeling magnetic particle imaging for dynamic tracer concentrations , submitted to Journal of Sensing and Imaging

I am currently working on inversion methods for magnetic particle imaging and as well as for
inverse problems in X-ray science. Moreover, I am interested in dynamic inverse problems
and regularization methods for these problems.

More generally my research interests are :

More generally my research interests are :

- Medical and biological imaging
- Modelling and simulation
- Regularization with sparsity constraints
- Dynamic inverse problems
- Bayesian inversion and approximation error modelling
- Coupled inverse problems in X-ray laser physics
- Phase retrival

**Prof. Martin Storath**

**Prof. Tobias Knopp, Dr. Martin Hofmann**

Sektion für Biomedizinische Bildgebung

Institut für Biomedizinische Bildgebung

Technische Universität Hamburg

**Dr. Wolfgang Erb**

Italy

**Prof.P. Maass, Dr. Tobias Kluth**

University of Bremen

**Dr. Andreas Hauptmann**

Great Britain

**Dr. Aku Seppänen**

Kuopio, Finland

**Prof. Nina Rohringer**

and Universiät Hamburg

**PD Dr. Guido Meier**

and Max Planck Institute for the Structure and Dynamics of Matter

**Prof. Dr. Ralf Röhlsberger **

and Universiät Jena

Togeher with Prof. Iske and Dr. Kluth, I will give a course on mathematical machine learning
during summer term 2021

The course will give an overview about machine learning including manifold learning, classification
methods, network trainging algortihms ans deep learning for inverse problems.

Proseminar for Bsc students based (summer term 2021)

Joint research seminar with Prof. Iske (every term)

Interested Msc students are welcome to participate and to give a talk in the seminar.

- Amol Bains, Variationelle Tikhonov-Regulariserungsverfahren für Entfaltungsprobleme, Bsc, University of Hamburg
- M.L. Bui, Das semiglatte Newton-Verfahren für Optimierungsprobleme
- F. Hansen, Proximale Punkt Verfahren für nichtglatte konvexe Optimierungsprobleme, Bsc, University of Hamburg
- A. Kryeziu, Parameter identification in electrical impedance tomography with multi-bang-regularization, Msc, University of Hamburg
- J. Marquardt, Die schnelle Wavelettransformation und Bildkompression, Bsc, University of Hamburg
- S. Rickert, Methoden für nichtglatte Optimierungsprobleme, Bsc, University of Hamburg

Online Bachelor course on numerical mathematics, University of Hamburg (winter 2020/21)

Online Master course on Inverse problems together with Prof. Trabs, University of Hamburg (summer 2020)

Online Bachelor course on optimization, University of Hamburg (summer 2020)

Seminar on mathematical imaging University of Hamburg (winter 2019)

Master course on Convex optimization: The course covers classical theory of convex optimization and splitting methods for structured minimization problems. University of Hamburg (summer 2019)

Lothar Collatz Seminar (together with Stefan Heitmann), University of Hamburg (summer 2019)

Master course on inverse problems: We will treat the classical theory of linear inverse problems. University of Hamburg (winter 2018/19)

Master course on computer tomography: This is the first part of the inverse problems course. University of Hamburg (winter 2018/19)

Bachelor course on optimization, University of Hamburg (summer 2018)

Lothar Collatz Seminar (together with Stefan Heitmann), University of Hamburg (summer 2018)

Course on computer tomography and short introduction to inverse problems, University of Hamburg (winter 2017/18)

Course on convex optimization in infinite dimesions, University of Hamburg (summer 2017)

Exercises on inverse problems, University of Eastern Finland (winter 2016/17)

course together with Prof. Kunis, University of Osnabrück (summer 2014)

2 tutorials, University of Bremen (winter 2012/13)

2 tutorials, University of Bremen ( summer 2012 )

- K. Scheffler, Enhancing matrix compression using convoluted tensor products of Chebyshev polynomials, Msc 2021, in cooperation with Prof. Knopp, Technical University Hamburg Harburg
- T. Abel, Shearlet based reconstruction for dynamic X-ray data, Msc 2021, University of Hamburg
- J. Grün, Bayesian inversion and artifact reduction in magneti particle imaging, Msc 2020, University of Hamburg
- L. Zdun, Stochastic first order methods for image reconstruction problems, Msc 2020, University of Hamburg
- C. Fichtlscherer, Regularized reconstructions of the single pixel Radon transform, Msc 2020, University of Hamburg
- L. Schönrock, Zeitbasierte Regularisierungsmethoden für medizinische Bilgebung, Bsc 2020, University of Hamburg
- I. Glöckner, Reconstruction methods for multi-color magnetic particle imaging, Msc 2019, University of Hamburg, supervision together with Dr. M. Möddel, Prof. T. Knopp (UKE HH Eppendorf)
- J. Muldoon, Visualisierung von Schallwellen mittels Lasterintrferomatrie und Radon-Transformation, Msc 2019, University of Hamburg, supervision together with Dr. Fischer (Institut für Musikwissenschaften, UHH)
- D. Hailu, Reconstructions for dynamic x-ray data with help of shearlets, Msc, University of Hamburg
- A. Geng, Improving calibration measurements in magnetic particle imaging by compressed sensing, Msc, University of Hamburg, superivision together with Dr. Martin Möddel (UKE HH Eppendorf)
- I. Pabon, Adaptive spatio-temporal regularization for robust nonlinear registration in artifact-affected 4D medical images, Msc, University of Hamburg, supervision together with Dr. Rene Werner (UKE HH Eppendorf)
- D. Hamann, Non-convex stochastic optimization, Bsc 2018, University of Hamburg
- I. Jumakulyyev, Joint motion estimation and image reconstruction by incorporating a priori edge information, Msc 2018, University of Hamburg
- L. Nawwas, Bias-reduction for sparsity promoting regularization in magnetic particle imaging, Msc 2018, University of Gdansk/University of Hamburg
- T. Yu, Optimization for HDR brachytherapy,Msc 2017, University of Hamburg, supervision together with Prof. Schlaefer, TUHH
- F. Lucchesi, Adversarial domain adaption for semantic segmentation Msc 2017, Unversity of Hamburg, supervision together with Prof. Vazquez, Universitat Autònoma de Barcelona
- T. Heiskanen,
**Superresolution in microscopy**, Bsc 2017, University of Eastern Finland - A. Pandey and I. Jumakulyyev,
**Optical flow estimation**, Summer project 2017, University of Hamburg - T. Heiskanen,
**Deconvolution in fluorescence microscopy**, Summer trainee 2016, University of Eastern Finland - P. Gralla,
**Mathematical methods for controlling ultra precise processes of machining**, Bsc 2012, Supervision together with Dr. I. Piotrowska, University of Bremen. - P. Welz,
**Regenerative chattering for micro cutting processes**, Bsc 2011, supervision together with Prof. P. Maass, University of Bremen.

Since 03 2017

09/2015 - 02 2017

Academy post-doctoral researcher in the Inverse Problems group at the Eastern University of Finland in Kuopio.

- Academy of Finland project "Approximation error modelling in large scale inverse problems in imaging" (2015-2018)

04/2013 - 08/2015

Post-doctoral researcher in the research group "Applied and Numerical Analysis" at the University Osnabrück working within the

- Helmholtz young investigator group "Fast algorithms for biomedical imaging" (Helmholtz Zentrum München)

01/2008 - 04/2013

Research assistant in the at the WG Industrial Mathematics at the Center for Industrial Mathematics at the University Bremen, working within the

- DFG priority program SPP1180, project "Mathematical methods for precision balancing of machine tool" (2008-2011)
- SFB "Micro cold forming" (2012-2013)

2008 - 2012

**Doctoral studies** in the research group "Industrial mathematics" at the University Bremen

2012: **Doctorate degree in mathematics, University Bremen **

- Thesis: "Regularization of Inverse Problems for Turning Processes"
- Supervisor: Prof Peter Maass

10/2001 - 08/2007

08/2007

**Diploma studies in mathematics **, University Rostock

Diploma degree in mathematics, University Rostock

- Thesis: "Numerical phase reconstruction" (Numerical phase retrieval)
- Supervisor: Prof Manfred Tasche

10/2002 - 08/2007

10/2006 - 07/2006

08/2007

** Diploma studies in physics**, University Rostock

Master studies in physics, Erasmus exchange program, University Nantes

Master degree in physics, University Nantes (France)

- Master thesis: "Energy loss of a parton in a quark-gluon-plasma"
- Supervisor: Stéphane Peigné

Department of Mathematics

Bundesstraße 55

20146 Hamburg

Office: Geomatikum 116

Phone: +49 -40 42838 4076

Mail: Christina.Brandt(ad)uni-hamburg.de

Fachbereich Mathematik

Bundesstraße 55

20146 Hamburg

Germany