Biomedical Technologies and Computing

This research area concerns the development of novel biomedical technologies and computing approaches for applications in biomedicine and biomedical engineering. TUMs unique expertise in these areas creates and ideal platform for synergies that ultimately leads to new biomedical devices, procedures, and analysis tools. These research projects have a particularly high relevance for numerous spin-off and start-up projects at UnternehmerTUM.

The following selected research topics list some examples of current activities that are pursued by the MSB principal investigators.

Computer-aided Medical Procedures

PI Nassir Navab

This research project focuses on computer-aided medical procedures and augmented reality. The work involves developing technologies to improve the quality of medical interventions and bridges the gap between medicine and computer science.  The research objective is to study and model medical procedures and introduce advanced computer integrated solutions to improve their quality, efficiency, and safety. We aim at improvements in medical technology for diagnosis and therapeutic procedures.

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PI Bernhard Wolfrum

This group focuses on printing technologies for life science and point-of-care applications. In particular, it is interested in bioelectronic interfaces and sensor arrays for the stimulation and recording of chemical and electrical signals in cellular networks. The goal is to develop microfluidic biohybrid devices to investigate network-scale phenomena including cellular signal propagation, lesion response, and progressive neurodegeneration.  

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Bio-inspired Information Processing

PI Werner Hemmert

The cochlear implant is the most successful neuroprostheses, and this project develops novel approaches for the advancement of neuroprostheses, where the main research focus lies on cochlear implants. They replace a full sensory organ and are able to restore hearing and especially speech understanding to a surprisingly high degree. The research success relies on the combination of theoretical concepts (models of the electrical excitation of neurons, sound processing in the auditory pathway), development of novel technology (e.g. binaural listening technology), objective measurements (recording of evoked neuronal potentials) and listening experiments, which is conducted in close collaboration with workgroups from the fields of biology, medicine and industry.

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Munich Compact Light Source (MuCLS)

PI Franz Pfeiffer

For some years now it has been possible to generate high-brilliance X-rays using ring-shaped particle accelerators (synchrotron sources). However, such installations are several hundred meters in diameter and cost billions of euros. As an alternative, the world’s first mini synchrotron – the Munich Compact Light Source (MuCLS) -  was installed in 2015 at Technical University of Munich (TUM). It can generate high-brilliance X-rays on a footprint measuring just 5 x 3 meters. The new unit will be used chiefly to research biomedical questions relating to cancer, osteoporosis, pulmonary diseases and arteriosclerosis.

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Audio Information Processing

PI Bernhard Seeber

The Audio Information Processing group investigates the processing of sound in the human auditory system and uses this knowledge to improve hearing devices and audio systems. Experiments on how we perceive sounds guide the group in building signal processing models of the hearing system. Such models form the basis for audio coding („mp3“) and hearing device processing, and help design product sound quality.

The focus of the group is on improving hearing devices through novel approaches for audio coding in neuronal prostheses (cochlear implants) and hearing aids. They are interested in the brain’s processing of acoustic scenes with multiple sounds and reverberation because these are particularly challenging situations for people with hearing impairment. 

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Measurements of Cell Function

PI Oliver Hayden

Cells are the smallest integral unit of life. However, integrated testing of cell function and cell-cell interaction on various time scales is an unmet clinical need. In a highly interdisplinary research field at the Chair of Biomedical Electronics we focus on workflow integration and biosensing methods for precision measurements of cell function. (a) A magnetoresistive sensor, adopted from hard disk read heads, is tuned for single cell analysis with respect to cell size, magnetic loading of cells, and cell morphology. (b) To minimize Si area for bedside Point-of-Care Testing we have designed a functionalized semiconductor package housing a 2×2 mm² sensor in an injection moulded microfluidic device. (c) The system balances magnetophoretic and hydrodynamic forces for precise and highly reproducible probing of single cells and thus could provide cell function testing in an integrated workflow at the bedside for fast clinical outcome.

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Medical Image Computing

PI Björn Menze

Our research is in medical image computing, exploring topics at the interface of medical computer vision, image-based modeling, and computational physiology with a strong application focus on oncology. Very broadly, we are interested in developing computational methods that will help in transforming the qualitative visual inspection of medical image data into a functional interpretation of the disease process. In this, we focus (1) on developing principled directions for integrating and abstracting information from complex multi-modal image data by using biophysical-statistical models, and (2) on further developing data-driven machine-learning approaches that provide means for embedding this model-driven analysis in a workflow capable of dealing with large clinical image data sets at the population scale. 

In the long term, this work will provide the computational methods necessary to infer function from structure by modeling clinically relevant functional anatomy, and the technical means for developing new (patho-) physiological models at the same pace as novel imaging methods to generate more and more specific insights into anatomy and function.

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