AI in Optoacoustics
Jüstel Lab
Head of the group: Dominik Jüstel
High-quality biomedical imaging needs reconstruction procedures that are accurate and efficient, and subsequent data analysis that is reliable and insightful.
At the group for “Artificial Intelligence in Optoacoustics (AI in OA)”, we develop computational methods for biomedical imaging and sensing based on sophisticated mathematical models. Our main focus is optoacoustic imaging, its combination with ultrasound imaging, and optoacoustic sensing. We also contribute to the analysis of the huge amount of data that is generated within Helmholtz Munich and in our research collaborations. Our group is a driving force for the translation of optoacoustic technology to the clinic by providing computational solutions for translational problems.
Selected Projects
In this collaboration with iThera Medical, we enable high optoaoustic image quality on the system in real time.
Multispectral optoacoustic tomography in combination with ultrasound (OPUS) is a powerful medical imaging modality that provides coregistered optical and acoustic contrast deep in tissue label-free and without ionizing radiation. We develop deep learning solutions to exploit the synergies between the two modalities and enable an optimal image quality on the system screen during the scanning procedure. This translational effort will greatly increase the value of OPUS imaging systems in everyday clinical practice.
Intelligent Optoacoustic Radiomics via Synergistic Integration of System Models and Medical Knowledge.
Radiomics – the extraction of medical information from imaging data via mathematics and data science – is on the verge of becoming a main player in clinical medicine. However, the current radiomics workflow lags behind the state-of-the-art in explainable artificial intelligence. We will integrate the whole imaging workflow – from imaging hardware to clinical interpretation – into an intelligent software environment, thereby realizing the transition from black box machine learning to intelligent radiomics. The clinical use case is imaging of peripheral nerves with optoacoustic tomography, which can visualize nervous tissue in unprecedented detail. The project, thus, has the potential to enable early detection of pathological changes in peripheral neuropathy, e.g., in conjunction with diabetes.
Postdoctoral fellow - Inverse Problems in Optoacoustics
Physician scientist - Clinical Epidemiology
Postdoc - Machine Learning and Data Analysis for Optoacoustic Sensing
Ph.D. Student
Ph.D. Student - Probabilistic Reconstruction and System Characterization
Postdoc
Ph.D. Student - Probabilistic Reconstruction and Fluence Modeling
Ph.D. Student - Data-driven Optoacoustics for Metabolism
Ph.D. Student - Tissue Characterization and Data Analysis in Optoaoustics
Ph.D. Student - Mathematical Modeling and Data Analysis for Optoacoustic Sensing
Ph.D. Student
Ph.D. Student
Ph.D. Student
Alumni (Ph.D. Student) - Image Reconstruction and Processing
Alumni (Ph.D. Student) - Machine Learning and Image Analysis for Biomedical Imaging
Guest
Ph.D. Student)
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