Postdoc Position Self-driving Ultrasound Imaging at Eindhoven University of Technology

Applicants are invited to apply for a postdoctoral scholar position in signal processing at Eindhoven University of Technology. Applications will be evaluated immediately.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in humans, causing several complications, including stroke and death. Treatment costs are estimated at 26 B$/year in the USA and over 637 M/year in the Netherlands alone. Catheter ablation to create pulmonary vein isolation has become the standard therapy for AF. However, optimal and safe ablation is hampered by the lack of tools for detecting fibrosis or scar tissue, and for assessing the quality of the ablation lesions. As a result, one-year single procedural success rates are disappointingly low (60%). Parallel to the growing need for a safe and cost-effective tool to monitor and assist cardiac ablation, ultrasound transducer technology has advanced into miniaturized implementations, opening up new diagnostic imaging opportunities that are cost-effective and perfectly suitable for intra-procedural use.

In this project we will devise a new imaging paradigm, termed "self-driving ultrasound", in which the ultrasound transmit driving schemes and beamforming algorithms are continuously adapted in a reinforcement learning framework to provide at any time the optimal tradeoff between various imaging properties (e.g. frame rate and resolution), disturbing artefacts, and system-level constraints on power and data-rates. Novel multi-parametric receive signal-processing approaches will be employed to enable quantification of local tissue properties relevant for ablation monitoring (e.g. viscoelasticity and vascular perfusion). The technology will be implemented on existing 2D intra-cardiac probes with a versatile ultrasound system.

The project is a collaboration between the Biomedical Diagnostics lab (BM/d) of the Eindhoven University of Technology (TU/e), Philips Research, and the Catharina Hospital Eindhoven. The project team is designed to combine extensive knowledge and expertise in ultrasound system design, signal analysis, modelling, implementation and interventional cardiology, with an elaborate clinical infrastructure. There is a long-standing collaboration between these partners, as part of a research consortium called the Eindhoven MedTech Innovation Center (E/MTIC), focusing on topics such as peri-operative monitoring and care, ultrasound-guided interventions, and physiology- and data-driven decision support models.

The Signal Processing Systems (SPS) group of the Electrical Engineering Department of the TU/e, chaired by Prof. Jan W.M. Bergmans, has a strong and longstanding international academic reputation in physiological monitoring of cardiac activity and ultrasound imaging as evidenced by the numerous international publications and awards. Prof. Massimo Mischi, coordinator of the Biomedical Diagnostic (BM/d) Lab within the SPS Group (www.bmdresearch.nl), has a strong track record in quantitative ultrasound imaging and biomedical signal processing.

In this context, we are seeking a highly motivated post-doctoral researcher with knowledge and interest in image acquisition physics (preferably ultrasound) and deep learning (specifically reinforcement learning). The position is available within the BM/d research lab, part of the Signal Processing Systems (SPS) group (Electrical Engineering department, TU/e), and it involves a strong collaboration within the E/MTIC consortium, i.e. with Philips Research and the Catharina Hospital.

More information on employment conditions can be found here: http://www.tue.nl/en/university/working-at-tue/working-conditions/ .

For more information about this position contact dr. ir. Ruud van Sloun, e-mail: at]tue.nl .

More information on employment conditions can be found here: http://www.tue.nl/en/university/working-at-tue/working-conditions/.

The selection process will start in March 2019 and will continue until the position gets filled. The position is fully funded and immediately available. The successful candidates are expected to start ASAP.

Deadline: As soon as possible
Apply Link:http://tinyurl.com/yysl2d3t

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