Simona Turco

Simona Turco

Assistant professor at the Biomedical Diagnostics lab, Signal Processing Systems group, Electrical Engineering, Eindhoven University of Technology

sidebar logo

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

European symposium on ultrasound contrast imaging 2024

less than 1 minute read

Published:

Looking back at two fantastic days at the European Symposium on Ultrasound Contrast Imaging 2024. Lots of inspiring talks from committed researchers who are pushing the envelope of contrast ultrasound for imaging and therapy. It was a pleasure to meet old colleagues and friends from all over the world and discuss future exciting collaborations. We also had the opportunity to present our most recent work on CUDI for uterine disorders (Ferenc Kandi, Eva J.E. de Bock) and the honor to be awarded the best poster for the biology/clinical session (Tinting Wang).

portfolio

Prediction of patient deterioration

In perioperative care, the timely prediction of patient deterioration has as a pivotal role in ensuring optimal patient outcomes and the efficient allocation of resources within hospital settings. The ability to foresee changes in a patient’s condition before they escalate into critical states is paramount for healthcare providers seeking to deliver high-quality and patient-centered care. In this line of research, we develop novel strategies for timely prediction of patients at risk.

Radiomics and radiogenomics of prostate cancer

Prostate cancer is the second most common form of cancer and one of the most lethal in western men. Timely and accurate diagnosis is crucial. Multiparametric MRI is currently the recommended imaging modality for prostate cancer. However, it is not sufficiently accurate to replace systematic biopsies. In this line of research, we extract quantitative parameters from multiparametric MRI and combine them by machine learning for improving prostate cancer diagnostics. Additionally, we investigate the link between relevant MRI features and genomic features of aggressive prostate cancer.

Quantitative contrast-enhanced ultrasound

Ultrasound contrast agents (UCAs) have significantly expanded diagnostic possibilities through the concurrent application of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. In this line of research, we develop quantitative analysis methods to extract multiple quantitative maps from DCE-US, reflecting complementary variables of underlying physiological processes. Using probabilistic frameworks based on emerging machine-learning methods, we aim to enhance the diagnostic accuracy of DCE-US imaging through the optimal combination of extracted complementary information.

Modeling and analysis of patient mechanical ventilation

Mechanical ventilation is a critical medical intervention employed to support patients with compromised respiratory function. While mechanical ventilation is essential in various clinical settings, including intensive care units and emergency situations, it is not without potential risks and complications. This line of focuses modeling and analysis of mechanical ventilation for better assessment of patient’s efforts with the aim of reducing potential risks.

Nanobubble-based contrast-enhanced ultrasound

Novel contrast agents composed of nanobubbles (~300 nm) are being developed to overcome the limitations of standard clinically-available ultrasound contrast agent. Because of their smaller size, nanobubbles can cross the vascular endothelium and reach targets beyond the vessel wall, opening up new avenues for assessment of vascular permeability and the expression of cancer-specific targets. In this line of research, we developed pharmacokinetic model describing the transport of the nanobubbles in the vasculature and their distribution in tissue, with the aim of extracting quantitative cancer biomarkers.

Hemodynamic monitoring

Hemodynamic monitoring in hospitals involves the continuous observation and assessment of blood circulation and cardiovascular function in patients. This process is crucial for understanding and managing conditions that affect the heart and blood vessels. In this line of research, we develop novel theoretical and experimental methods for assessing hemodynamics function, using standard clinical equipment.

publications

research

Cancer imaging


Cancer is a global health challenge. Timely and accurate detection and diagnosis, together with effective therapy monitoring are essential in the fight against cancer. Based on the established link between cancer and the formation of (neo)vessels to support tumor growth (angiogenesis), several imaging modality have been developed to detect early angiogenic changes. Non-invasive, radiation-free ultrasound and MRI imaging are particularly interesting for angiogenesis imaging. However, mostly qualitative assessment is currently performed in the clinical routine, possibly missing important information hidden in these rich spatio-temporal imaging sets. In this line of research, we develop methods for quantification of MRI and US imaging, and extraction of effective cancer biomarkers. I strive to combine model-driven and data-driven approaches to obtain interpretable, physics-based biomarkers for improving cancer diagnostics.

Perioperative monitoring


Patient monitoring in perioperative care involves the continuous observation and assessment of individuals before, during, and after surgical procedures. This comprehensive monitoring is crucial to ensure the patient’s safety, well-being, and optimal recovery. Throughout the perioperative period, various vital signs and parameters are closely observed, including heart rate, blood pressure, oxygen saturation, respiratory rate, and body temperature. A crucial aspect of patient monitoring in perioperative care involves the implementation of systems for predicting and detecting signs of patient deterioration. Early identification of potential issues allows for prompt intervention and can significantly improve patient outcomes.

talks

teaching

Statistical Signal Processing

Master course, Eindhoven University of Technology, masters EE and AI&ES, 2018

As variability and noise affect all measurements, all signals are inherently stochastic variables. Deterministic signal processing approaches are thus inadequate for many real-world applications.  Whether a radar signal to detect a plane, an electrocardiogram to diagnose heart conditions, or a GPS signal to guide an autonomous car, virtually every engineer deals with real-world signals affected by several sources of noise and interference. Understanding how to handle and process signals in the presence of uncertainty, e.g., “random” signals, is thus fundamental for every student aiming at becoming an engineer. This course provides the basic tools necessary for understanding and processing random signals.