Dr. Philipp Köhler leads the research group Medical Sensors at the Institute of Biological and Medical Imaging (IBMI) at Helmholtz Munich. His research focuses on advancing optoacoustic sensor technologies for clinical applications – with a particular emphasis on physics, spectroscopy, and biomedical imaging.

His academic journey began with a Bachelor's and Master's degree in Physics at the Technical University of Munich, complemented by courses in Biomedical Physics. He then received a prestigious scholarship from the Cavendish Laboratory at the University of Cambridge, where he earned a second Master's degree in Nanotechnology and completed his PhD in Physics, specializing in spectroscopy and optics.

During his time in Cambridge, Dr. Köhler gained hands-on research experience at Cancer Research UK, located on the grounds of Addenbrooke’s Hospital. There, he investigated fluorescent DNA origami structures as markers for multispectral optoacoustic tomography (MSOT).

To strengthen the transfer of knowledge from research to application, he completed innovation and entrepreneurship courses at the Cambridge Judge Business School and worked at the London-based start-up Cortirio, which develops wearable devices for detecting traumatic brain injuries using functional near-infrared spectroscopy (fNIRS).

Fascinated by the potential of MSOT technology, Dr. Köhler joined Helmholtz Munich at the end of 2019. Since then, he has driven the development of optoacoustic sensors – by securing funding, developing new prototypes, organizing clinical studies, and conducting market analyses. In September 2021, he took over the leadership of the Medical Sensors group.

Originating from optoacoustic imaging, Dr. Köhler and his team are developing novel optoacoustic sensors that bring the advantages of imaging modalities to a much greater patient cohort by enabling low-cost measurements in a much smaller and portable form-factor. Existing imaging methods are mostly universal tools that solely create an image, but rely on expert medical knowledge for an accurate interpretation of the results. 

This group’s sensors are specifically designed for each medical indication and use advanced data analysis methods such as Machine Learning and AI for an accurate and objective classification of diseases and their progression without the need for imaging. Relevant applications include diabetes and its complications, cardiovascular disease or dermatology, where an early and objective detection enables a more targeted and effective treatment.

Medical Sensors

Optics

Data Analysis

Prototyping

Knowledge Transfer

Market Research and Commercialization