Biomedical signal processing/wavefield imaging

Contact: Rob Remis

Signal processing and imaging (estimation and detection for diagnostics and therapy), in particular Magnetic Resonance Imaging (MRI) from EM modeling and instrument design to signal processing, image formation (numerical inversion techniques) and tissue parameter estimation, with applications to medical diagnostics in collaborations with university hospitals.
Biomedical signals estimation and detection: Estimation of signal parameters from multichannel signals (e.g. ECG); detection and localization of medical conditions such as heart fibrillation, epilepsy.

Projects under this theme

Tensor techniques to improve the analysis of (3D+time) ultrasound images

Tensor-based AI methods for biomedical signals

Develop smart compressive coding masks to make 3D ultrasound imaging cheap and widely applicable.

Developing a novel brain imaging paradigm combining functional ultrasound and EEG

Part of a larger program (with Erasmus MC) to unravel and target electropathology related to atrial arrhythmia

Development of a low-cost MRI scanner including processing to diagnose hydrocephalus

Atrial Fibrillation FIngerPrinting: Spotting Bio-Electrical Markers to Early Recognize Atrial Fibrillation by the Use of a Bottom-Up Approach

Using Krylov subspace reduction techniques to solve wave field problems in complex media (resonanting nano-scale devices and seismic exploration)

Modeling and analyzing the effect of high permittivity pads in MRI imaging