Revolutionizing High-Precision Non-Invasive Brain Treatments with Advanced Numerical Modeling

Transcranial Focused Ultrasound (tFUS) is a groundbreaking technology that can treat neurological conditions like Parkinson’s disease, epilepsy, and even enable targeted drug delivery—without invasive surgery. However, accurately focusing ultrasound waves deep inside the brain is challenging, especially without the use of expensive MRI guidance. Current alternatives lack real-time verification, making treatments less precise and potentially less effective.

This project aims to develop a new, cost-effective method for pinpointing the ultrasound focal spot using a combination of acoustic sensors and advanced numerical modeling. By leveraging mathematical optimization techniques, we will create patient-specific computational models of the brain’s complex, heterogeneous structure. These models will predict how ultrasound waves travel through different tissue types, enabling real-time adjustments for precise targeting.

Our approach includes two key innovations. First, we will develop an inverse optimization framework to refine estimates of the brain’s acoustic properties, using data from an array of transcranial acoustic sensors. Second, we will implement nonlinear numerical methods to iteratively adjust the ultrasound beam’s focus, ensuring it reaches the intended target with high accuracy. These techniques will replace the current trial-and-error approach, significantly improving treatment safety and efficiency.

By integrating numerical modeling, optimization algorithms, and experimental validation, this project will advance the field of non-invasive brain therapies. The results will help make tFUS more affordable and accessible, ultimately benefiting patients worldwide. This interdisciplinary research offers an exciting opportunity for graduate students interested in medical physics, computational modeling, and biomedical engineering, with real-world applications in cutting-edge healthcare technology.

The Engineering science, computer science and imaging laboratory

Created in 2013, the laboratory brings together researchers of the University of Strasbourg, the CNRS (French National Center for Scientific Research), the ENGEES and the INSA of Strasbourg in the fields of engineering science and computer science, with imaging as the unifying theme.

With around 650 members, ICube is a major driving force for research in Strasbourg whose main areas of application are biomedical engineering and the sustainable development.

The laboratory is a member of the Carnot Telecom & Société numérique institute, thanks to its close relationship with Telecom Physique Strasbourg.

Ideal Candidate Profile for This Project

We are looking for a motivated graduate student with a passion for interdisciplinary research and problem-solving. This project sits at the intersection of computational modeling, biomedical engineering, and medical physics, offering an exciting opportunity to develop cutting-edge methods for non-invasive brain treatments.

Key strengths of a successful candidate:

Interdisciplinary mindset – Ability to integrate knowledge from physics, engineering, and medical imaging.

Strong problem-solving skills – Creativity and critical thinking to tackle complex challenges in ultrasound-based therapies.

Programming experience – Familiarity with Python, MATLAB, C++, or similar languages for computational modeling and data analysis.

Mathematical and computational abilities – Interest in numerical methods, optimization, and inverse problems.

Understanding of wave propagation – Basic knowledge of acoustics or ultrasound physics (or a willingness to learn).

Experimental curiosity – Enthusiasm for validating models with real-world data, including working with sensors and imaging systems.

This project is ideal for candidates from biomedical engineering, physics, applied mathematics, electrical engineering, or related fields. Whether you’re a computational scientist looking to apply your skills in medical applications or an experimentalist interested in numerical modeling, this research offers a unique opportunity to make an impact in non-invasive healthcare technology.