Janek Gröhl | Vision Lab

The quantification of absolute molecular concentrations can be considered the holy grail of multispectral photoacoustic imaging. I am interested in applying machine learning methods to investigate this challenge, as solving it would enable accurate spectral unmixing of local blood oxygenation and open numerous exciting clinical applications.

Brief CV

BSc Medical Informatics, Heilbronn University of Applied Sciences, Germany (2011-2014)
MSc Medical Informatics, Heidelberg University, Germany (2014 – 2016)
PhD Medical Informatics, German Cancer Research Center, Heidelberg, Germany (2016 – 2020)
Postdoc Photoacoustic Imaging, German Cancer Research Center, Heidelberg, Germany (2020)

Publication Highlights

Learned spectral decoloring enables photoacoustic oximetry

Published to Nature Publishing Group

J Gröhl, T Kirchner, TJ Adler, L Hacker, N Holzwarth, A Hernández-Aguilera, MA Herrera, E Santos, SE Bohndiek, L Maier-Hein, 11(1):1-12, 2021.

Deep learning for biomedical photoacoustic imaging: A review

Published to Elsevier

J Gröhl, M Schellenberg, K Dreher, and L Maier-Hein, Photoacoustics, 100241, 2021.

IPASC: A community-driven consensus-based initiative towards standardisation in photoacoustic imaging

Published to IEEE

SE Bohndiek, BT Cox, J Joseph, J Gröhl, L Hacker, S Morscher, WC Vogt, 2020 IEEE International Ultrasonics Symposium (IUS)

Context encoding enables machine learning-based quantitative photoacoustics

Published to International Society for Optics and Photonics

T Kirchner*, J Gröhl*, and L Maier-Hein, Journal of biomedical optics, 23(5):056008, 2018.

Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics

Published to Multidisciplinary Digital Publishing Institute

J Gröhl, T Kirchner, T Adler, and L Maier-Hein, Journal of Imaging, 4(12):147, 2018.

Best conference attended so far

SPIE Photonics West, Photons plus Ultrasound, February 2019