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Personal profile

Quote

"I work in artificial intelligence from sensor to interpretation – achieving better, faster and widely-accessible medical diagnostics through sensing systems that efficiently learn how to optimally sense, process, and interpret real-world signals."

Research profile

Ruud van Sloun is an Assistant Professor in the Signal Processing Systems group of the Electrical Engineering department at Eindhoven University of Technology (TU/e). He works on advanced and intelligent sensing and signal processing algorithms, with a special focus on artificial intelligence in diagnostic ultrasound imaging.
He has a background in probabilistic signal processing for ultrasound-based cancer localization and exploiting signal structure and models to derive optimal estimators. After his PhD, this background has become intertwined with artificial intelligence (AI) and deep learning, to develop powerful signal processing solutions that efficiently leverage data and model-based signal structure. Applications span from AI-driven ultrasound beamforming and image formation to clutter suppression and super-resolution imaging.
Van Sloun has contributed to over 50 scientific publications and 4 patents. In 2019, he received a RUBICON grant on deep learning for next-gen ultrasound from The Netherlands Organization for Scientific Research (NWO).

 

Academic background

Ruud van Sloun studied Electrical Engineering at Eindhoven University of Technology (TU/e) where he received cum laude MSc and PhD degrees in 2014 and 2018, respectively. In January 2018, he joined TU/e as an Assistant Professor. Since then, he has been working on artificial intelligence and signal processing for diagnostic (imaging) applications, spending a significant amount of time at foreign research institutes. Van Sloun also acts as a consultant for Philips Research, where he works one day per week.

Affiliated with

Affiliated with

  • Philips Research

 

Partners in (semi-)industry

  • Philips Research
  • Onera

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Research Output

Contrast-enhanced ultrasound tractography for 3D vascular imaging of the prostate

van Sloun, R. J. G., Demi, L., Schalk, S. G., Caresio, C., Mannaerts, C., Postema, A. W., Molinari, F., van der Linden, H. C., Huang, P., Wijkstra, H. & Mischi, M., 1 Dec 2018, In : Scientific Reports. 8, 1, 8 p., 14640.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
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  • 4 Citations (Scopus)
    37 Downloads (Pure)

    Compressed sensing for ultrasound computed tomography

    Sloun, van, R. J. G., Pandharipande, A., Mischi, M. & Demi, L., 2015, In : IEEE Transactions on Biomedical Engineering. 62, 2, p. 1660-1664

    Research output: Contribution to journalArticleAcademicpeer-review

  • 18 Citations (Scopus)
    5 Downloads (Pure)

    Entropy of ultrasound-contrast-agent velocity fields for angiogenesis imaging in prostate cancer

    van Sloun, R. J. G., Demi, L., Postema, A. W., de la Rosette, J. J. M. C. H., Wijkstra, H. & Mischi, M., 1 Mar 2017, In : IEEE Transactions on Medical Imaging. 36, 3, p. 826-837 12 p., 7745886.

    Research output: Contribution to journalArticleAcademicpeer-review

  • 14 Citations (Scopus)

    Ultrasound-contrast-agent dispersion and velocity imaging for prostate cancer localization

    van Sloun, R. J. G., Demi, L., Postema, A., de la Rosette, J. J. M. C. H. J., Wijkstra, H. & Mischi, M., Jan 2017, In : Medical Image Analysis. 35, Januari 2017, p. 610-619 10 p.

    Research output: Contribution to journalArticleAcademicpeer-review

    Open Access
  • 30 Citations (Scopus)
    1 Downloads (Pure)

    Viscoelasticity mapping by identification of local shear wave dynamics

    van Sloun, R. J. G., Wildeboer, R. R., Wijkstra, H. & Mischi, M., Nov 2017, In : IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 64, 11, p. 1666-1673 8 p., 8015181.

    Research output: Contribution to journalArticleAcademicpeer-review

  • 9 Citations (Scopus)
    2 Downloads (Pure)

    Courses

    Medical ultrasound

    1/09/13 → …

    Course

    Student theses

    Adversarial deep learning in super resolution ultrasound

    Author: Jeukendrup, K., 31 Jan 2019

    Supervisor: van Sloun, R. (Supervisor 1)

    Student thesis: Master