Daniel Rückert

Computer Science

Anyone who has suffered a sport injury knows the form: lie as inert as possible in an MRI scanner while a computer processes the incoming information, point by point, layer by layer, until an image emerges. And that all takes quite a while. It gets even more complicated if the subject moves during the scan due to a heart beating, an artery pulsating or a foetus moving around.
With the aid, amongst others, of artificial neural networks, Daniel Rückert has crucially improved the quality of medical imaging – because humans are immediately able to perceive movement and place it in an overall context whilst machines still have to learn to piece together objects and movements, differentiate them from their environment and then classify them. To this end, they have to be fed the relevant algorithms and vast amounts of data. One of Daniel Rückert’s achievements is to “teach” neural networks to produce a three-dimensional image of the brain and to recognise tumours and lesions. He has also developed a procedure that accelerates MRI image reconstruction of unborn babies without interfering with the image.
At the Technical University of Munich (TUM), the aims are to drive the implementation of Rückert’s imaging techniques and establish an interdisciplinary research focus in medical imaging.

Brief bio

Born in Germany, Daniel Rückert initially studied computer science at TU Berlin and completed his doctorate at Imperial College London, United Kingdom, in 1997. Having worked as a postdoc at King’s College London, he returned to Imperial College in 1999 where he was appointed to a professorship in visual information processing. Since 2016, he has also been the head of the Department of Computing. He is a fellow or member of numerous academies such as the British Computer Society and the IEEE (Institute of Electrical and Electronics Engineers). In September 2020 he took up his position as a Humboldt Professor at the Technical University of Munich (TUM).

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