DC 9 – Developing a virtual twin of the spine based on clinical images including the intervertebral disc

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:
Politecnico di Torino (POLITO), Italy.
Supervisory team:
Prof Cristina Bignardi (PhD supervisor, POLITO), Prof. Silvia Budday (PhD co-supervisor, FAU), Dr Alessandra Aldieri (PhD co-supervisor, POLITO), Dr Antoine Perrier (secondment host, TwinSight).
Enrolment in Doctoral School:
Enrolled in the Department of Mechanical and Aerospace Engineering (POLITO, Bioengineering and Medical-Surgical Sciences PhD program) and at the Department of Mechanical Engineering (FAU).

3. PhD project description:

This PhD project will focus on developing patient-specific image-based virtual twins of the spine, including detailed finite element models of the ageing intervertebral disc (IVD) informed by dedicated mechanical tests and clinical images. The PhD overall objective will be the development of personalized image-based finite element models of spine segments, with heterogeneous mechanical properties for both the vertebrae and intervertebral discs, together with their validation against experimental data. Specifically, the candidate will work on the construction of FE models of multi-vertebrae human spine segments starting from medical images such as MRI and CT. The candidate will also use experimental data (e.g. micro/nano indentation on IVD portions, flexo-compression tests on multi-vertebrae specimens, histological analyses) on healthy and degenerated IVD specimens and on multi-vertebrae human spine segments. The possibility to identify associations between MRI-based markers and mechanical properties of the IVD will also be investigated.

A successful project will result in novel image-based patient-specific finite element models of the spine, mathematical relationships linking IVD information from clinical images (e.g. MRI) to its mechanical properties, and a curated dataset of biomechanical properties of spine segments from clinical imaging (e.g. CT, MRI) and mechanical tests.

4. Planned secondments:

  • FAU: Aims to allow the DC to integrate methodologies for finite element modelling of human soft tissues as well
    as to benefit from the expertise of Prof Silvia Budday in computational soft tissue biomechanics.
  • TwinSight: Opportunity for the DC to gain knowledge on the integration of in silico technologies in an
    industry setting.

5. Essential requirements:

  • You have completed a master’s degree in Biomedical/Mechanical/Structural/Civil Engineering or possess
    corresponding qualifications that could provide a basis for successfully completing a doctorate.
  • Specialization in Biomechanics, with strong competences in finite element modelling and advanced constitutive
    laws implementation will be beneficial.
  • You have a keen interest in patient-specific modelling and programming.
  • 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 the host institution’s country 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 regression analysis to identify relationship between IVD relaxation times from MRI and
    IVD mechanical properties, mechanical testing (macro- and micro-scale), Finite element models construction from
    clinical images (CT, MRI), nonlinear finite element modelling, and/or model validation against experimental data
    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.