If a picture is worth a thousand words, what is a model worth?
Simona Turco is an assistant professor at the Signal Processing Systems group (department of Electrical Engineering), and member of the Biomdical Diagnostics (BM/d) research lab and of the Eindhoven MedTech Innovation Center (e/MTIC). Her research focuses on quantitative model-driven analysis of medical images and biosignals, with focus on oncology and perioperative care. As cancer growth and aggressiveness relate to angiogenesis, i.e., the formation of chaotic network of leaky microvessels, her research in cancer diagnostics is devoted to developing novel imaging solutions for assessment of angiogenesis. To go beyond structural imaging, ultrasound and MRI dynamic imaging with injection of contrast agents are used to extract structural, functional, and molecular information on angiogenic vasculature at different spatial and temporal scales. In the context of perioperative care, Turco's research focuses on model-based analysis of biosignals for accurate prognosis and timely identification of patients at risk. By combining model-based feature extraction with data-driven approaches for optimal feature selection, she aims at developing effective risk prediction models to support clinical decision making. In general, Turco aims at facilitating the translation of research, driven by industrial and clinical needs, through tight collaborations with top healthcare partners.
Simona Turco obtained her MSc in Biomedical Engineering from the University of Pisa (Italy) in 2012 summa cum laude. In 2015, she obtained a professional doctorate in engineering (PDEng) in Healthcare system design from the Stan Ackermans Institute at University of Technology (TU/e). Here, she also completed her Phd in 2018, with thesis entitled “Pharmacokinetic modeling in cancer: from functional to molecular imaging of angiogenesis”. Since then, she is a post-doctoral researcher at the Biomedical Diagnostics (BM/d) research lab, focusing on quantitative model-driven analysis of bio-signals.
Evaluation of dispersion MRI for improved prostate cancer diagnosis in a multicenter studyAmerican journal of Roentgenology (2018)
Dynamic velocity vector and relative pressure estimation in the left ventricle with dynamic contrast-enhanced ultrasound of low frame rates13th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2018 (2018)
Pharmacokinetic modeling in cancer : from functional to molecular imaging of angiogenesis(2018)
Quantification of contrast kinetics in clinical imaging(2018)
On the validity of the first-pass binding model for quantitative ultrasound molecular imaging2017 IEEE International Ultrasonics Symposium (2017)
- Statistical signal processing
No ancillary activities