DC 6 – Probabilistic failure risk assessment in ascending thoracic aortic aneurysms

1. Overview of the research programme:

InSilicoHealth is an innovative Doctoral Network (DN) with the ambition to train a new generation of outstanding
Doctoral Candidates (DC) that will become effective translators of the rapidly evolving digital technology to tackle
existing and future challenges related with healthy ageing in Europe. The research focus of this DN lies in three
key domains: the brain, heart, and musculoskeletal (MSK) systems. In the realm of digital technology, InSilicoHealth
specifically focuses on virtual human twin (VHT) technology to enhance our understanding of the age-related adaptive
changes of the complex human body through predictive multi-scale simulations. The research methodology employs
knowledge-driven models enhanced by advanced data-driven inference techniques to optimize the health potential of
older individuals.

2. Individual PhD Project Information:

Host institution
KU Leuven, Belgium
Supervisory team
Prof. Nele Famaey (PhD supervisor, KU Leuven), Dr Mathias Peirlinck (PhD co-supervisor, TUDelft), Prof. Patrick
Segers (PhD co-supervisor, UGent), Prof. Christian Gasser (secondment host, Vascops).
Enrolment in Doctoral School
Enrolled in the Arenberg Doctoral School for Science, Engineering & Technology (KU Leuven) and the Graduate
School of the TU Delft Faculty Mechanical, Maritime and Materials Engineering (TUDelft).

Host institution: KU Leuven, Belgium

3. PhD project description:

This PhD project will focus on defining a prospective patient-specific failure criterion for ascending thoracic aortic aneurysms, based on non-invasive patient measurements and retrospective data of patients. The objectives of the project are: 1) Create a framework to estimate a patient-specific probabilistic risk of rupture based on biomechanical criteria to improve the outcome of clinical decision-making; 2) Perform uncertainty quantification and uncertainty propagation activities for the computational framework; 3) Define a surgical decision-making framework.

A successful project will improve failure criterion for ascending thoracic aortic aneurysms correlating to patient-specific risk factors replacing the current generic maximum diameter criterion approach, will provide a novel hybrid modelling approach for the computational workflow, improve our understanding of the impact of uncertainty from patient-specific measurements on the computational framework and generate curated dataset of mechanical properties of the aorta in an elderly patient population.

4. Planned secondments:

  • TUDelft : Focused on gaining knowledge on data-driven modelling techniques for integration into their
    framework.
  • Vascops : It will provide hands-on experience for the DC on an already commercially available digital
    interdisciplinary system combining medical image processing with biomechanical analysis for abdominal aneurysms.

5. Essential requirements:

  • You have completed a master’s degree in Biomedical Engineering, Mechanical Engineering, Aerospace Engineering,
    Computational Physics, Applied Mathematics, or a related field, or possess corresponding qualifications that
    could provide a basis for successfully completing a doctorate.
  • You have a keen interest in cardiovascular modeling, computational soft tissue biomechanics and cardiovascular
    (patho)physiology
  • You have proven your proficiency in English language equivalent to B2 level.
  • You did not reside or carry out your main activity (work, studies, etc.) in Belgium for more than 12 months in
    the three years before 1st of January 2025.
  • You are ambitious, well organized, a team player, and have excellent communication skills.
  • You can work independently and have a critical mindset.
  • You are a pro-active and motivated person, eager to participate in network-wide training events, international
    mobility, and public dissemination activities.
  • Previous experience in hybrid modelling, multi-axial biomechanical tissue characterization, parameter
    optimization, nonlinear continuum mechanics, finite element analysis, constitutive model development and/or
    probabilistic modelling and multiple regression analysis is not required but considered a plus.

6. Application requirements:

  • Curriculum vitae.
  • Motivation Letter, including a clear indication of the preferred DC position(s) within InSilicoHealth Doctoral Network if the applicant postulates for multiple positions.
  • Academic records (grades) for Bachelor and Master degrees.