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Computational Imaging Research Lab (CIR)

CIR is an interdisciplinary research lab bringing together scientists in machine learning, imaging, medicine, and biology, developing novel methods to understand disease and to improve precision care. 

CIR is a division of the Department of Biomedical Imaging and Image-guided Therapy at the Medical University of Vienna. We are curious about how machine learning has to evolve to make an impact in healthcare. We believe that it has to support treatment decisions, but also needs to advance our understanding of the underlying mechanisms, as in the future it will gain a role in the development of novel treatment strategies. 

CIR is home to a diverse group of international researcher and three PIs leading their research groups in different areas of machine learning, medical imaging and precision medicine. It is closely linked to many clinical disciplines including radiology, oncology, surgery, paediatrics, pathology and neuroscience. We develop methods to predict disease course and treatment response in breast-, and lung cancer, identify novel treatment targets, or model the reorganization of the brain during disease. We are working with all kinds of imaging incuding radiology, nuclear medicine, pathology, ultrasound, or microscopy data. Sometimes, we perform basic research exploring early brain development, or the evolution of the human brain over the last 77 million years. Lately, we are linking machine learning and simulation in liver disease to bridge the gap between large-scale observations, and mechanistic understanding of disease, and are looking to integrate continually collected fitness tracker data with clinical imaging

Spin-off companies of our members are developing software for clinical radiology, and accurate large-scale clinical annotation campaigns.

Two new papers proposing new ML methods for MR spectroscopy reconstruction

Two new papers resulting from collaborations of CIR, the MR excellence center, MGH, and MIT propose new machine learning methods for the reconstruction of MR spectroscopy imaging (MRSI) data. Deep-ER uses deep learning for high resolution reconstruction of MRSI to assess whole brain neurometabolism, WALINET proposes a method for the removal of water and lipid signals in Proton MRSI data while preserving metabolite signals. 

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Project start of AutoPIX: Imaging and AI for Arthritis Patients

The kick-off meeting of AutoPiX happened on Dec. 10th 2024 in the Josephinum in the heart of Vienna. The project will connect imaging and AI to improve the care of patients with arthritis. It will develop novel technologiy in automated image analysis, therapy monitoring and prediciton, and the creation of foundation models to boost science and care in the area of arthritis. 

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