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Book chapter

    Andres, M., Blum, H., Brandt, C., Carstensen, C., Maass, P., Niebsch, J., Rademacher, R., Ramlau, R., Schröder, A., Stephan, E.-P., and Wiedemann, S.: Process Machine Interactions, Adaptive finite elements and mathematical optimization methods. Lectures Notes in Production Engineering. Springer, 2013.
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.

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Conference proceedings (reviewed)

    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., MaaÃ�, 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.

    Energy loss of a heavy quark produced in a finite-size quark-gluon plasma, volume 34, 2007.
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.

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Find my papers on researchgate

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.

Actual research projects on MPI

Bimodal reconstructions for MRI-MPI, joint work with Christine Bathke, Tobias Kluth and Peter Maass

Joint reconstruction in multi-color MPI, joint work with Martin Storath, 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

Visit Network Webpage

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.

    Bathke, C.; Kluth, T.; Brandt, C.; Maass, P.: Improved image reconstruction in magnetic particle imaging using structural a priori information, accepted by IJMPI
online availible here

    Brandt, C.; Seppänen, A. : Recovery from errors due to domain truncation in magnetic particle imaging -- approximation error modeling approach, in preparation for submission

I am currently working on inversion methods for magnetic particle imaging and as well as for deconvolution problems in fluorescence microscopy. Moreover, I am studying how to use shearlets for sparse reconstruction in photoacoustic tomography.

More generally my research interests are :

Medical and biological imaging

Modelling and simulation

Regularization with sparsity constraints

Bayesian inversion and approximation error modelling

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