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Dominik Jüstel - Computation & Machine Learning
Dominik Jüstel’s research lies at the interface between computational mathematics and biomedical imaging with a focus on clinical applications of optoacoustic imaging and sensing. Combining the unique abilities of optoacoustic technology with computational tools, Dominik and his team extract clinically relevant information from imaging and sensing data.
Dominik Jüstel studied mathematics and informatics at the Technical University of Munich, graduating at the Chair for Mathematical Modeling of Biological Systems at TUM and the Institute for Biomathematics and Biometry at Helmholtz Munich. He received his doctoral degree (Dr. rer. nat.) with highest distinction for his work on mathematical models and design problems in molecular X-ray diffraction imaging at the Chair for Analysis at the mathematics faculty of TUM. Dominik holds a tenure track position for ‘AI in optoacoustics’ at the Institute for Biological and Medical Imaging and the Institute for Computational Biology at Helmholtz Munich, and is also affiliated with the Chair for Biological Imaging at TUM. With his group at the interdisciplinary research center TranslaTUM, he works on computational methods and advanced data analysis for various optical and optoacoustic imaging and sensing modalities. Collaborating with multiple clinical institutions and industrial partners, his group is a driving force for the translation of optoacoustic technology to the clinic by providing computational solutions for translational problems.
- VIP+ Validation Grant (2022)
- ERC Starting Grant (2021)
- Medical Valley Award (2020)
Longo, A., Justel, D., Ntziachristos, V. (2022). Disentangling the frequency content in optoacoustics. IEEE Trans Med Imaging PP,
Kukacka, J., Metz, S., Dehner, C., Muckenhuber, A., Paul-Yuan, K., Karlas, A., Fallenberg, E.M., Rummeny, E., Justel, D., Ntziachristos, V. (2022). Image processing improvements afford second-generation handheld optoacoustic imaging of breast cancer patients. Photoacoustics 26, 100343.
Dehner, C., Olefir, I., Chowdhury, K.B., Justel, D., Ntziachristos, V. (2022). Deep-learning-based electrical noise removal enables high spectral optoacoustic contrast in deep tissue. IEEE Trans Med Imaging PP,
Chowdhury, K.B., Bader, M., Dehner, C., Justel, D., Ntziachristos, V. (2021). Individual transducer impulse response characterization method to improve image quality of array-based handheld optoacoustic tomography. Opt Lett 46, 1-4.
Yang, H., Justel, D., Prakash, J., Karlas, A., Helfen, A., Masthoff, M., Wildgruber, M., Ntziachristos, V. (2020). Soft ultrasound priors in optoacoustic reconstruction: Improving clinical vascular imaging. Photoacoustics 19, 100172.
Longo, A., Morscher, S., Najafababdi, J.M., Justel, D., Zakian, C., Ntziachristos, V. (2020). Assessment of hessian-based Frangi vesselness filter in optoacoustic imaging. Photoacoustics 20, 100200.
Chowdhury, K.B., Prakash, J., Karlas, A., Justel, D., Ntziachristos, V. (2020). A Synthetic Total Impulse Response Characterization Method for Correction of Hand-Held Optoacoustic Images. IEEE Trans Med Imaging 39, 3218-3230.
Justel, D. (2018). The Zak transform on strongly proper G-spaces and its applications. Journal of the London Mathematical Society 97, 47-76.
Justel, D., Friesecke, G., James, R.D. (2016). Bragg-von Laue diffraction generalized to twisted X-rays. Acta Crystallogr A Found Adv 72, 190-6.
Friesecke, G. James, R. D., Justel, D. (2016). Twisted X-Rays: Incoming Waveforms Yielding Discrete Diffraction Patterns for Helical Structures. SIAM Journal on Applied Mathematics 76, 1191-1218.