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.
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
homonstock - stock.adobe.comIn 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.
Logo European Research Council; ERCIntelligent 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.
homonstock - stock.adobe.comIn 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.
Logo European Research Council; ERCIntelligent 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.
Our Scientists
Recent Publications
Read more2023 Nature Machine Intelligence
A deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound
2022 Photoacoustics
2022 IEEE Transactions on Medical Imaging
