2023-2026

Numerical abacuses applied to the orthodontic field, a tool dedicated to the practitioners for the optimization of the choice of shape memory alloys archwires

Orthodontics aims at treating malocclusions mainly with fixed appliances, composed of brackets and archwires. Wires’ sections and dimensions evolve with the leveling of the dental arches according to a sequence of archwires chosen by the practitioner. Mechanical loads initiate dental displacement but can also be iatrogenic if they are excessive. The scientific problem is that practitioners do not have a precise way to quantify these mechanical loads.

  • PhD Student: Mouhamad Abou Hamdan
  • Supervisors: S. Touchal, D. Wagner, H. Jmal

Robot-assisted, focused ultrasound device for volumetric Blood-Brain-Barrier opening

Blood-brain barrier (BBB) is a natural physiological barrier that prevents pharmaceutical drugs from entering brain tissue. Focused ultrasound (FUS) can safely and temporarily open the BBB in a non-invasive manner to deliver drugs locally into the brain. The objective of this project is to develop a robotic-assisted FUS system for opening the BBB in extended volumes in the brain.

  • PhD student: Guilherme da Costa Correia
  • Supervisors: J. Vappou, F. Nageotte

Study and numerical analysis of femoral varus osteotomy by lateral opening

The Varus Femoral Osteotomy (VFO) is a recent surgical intervention correcting an anatomical misalignment. This surgery requires not only specific patient preoperative parameters but also the mastery of the surgical procedure by the surgeon. This project aims to develop a numerical and experimental twin of the surgery, from planning to the resumption of walking by integrating the consolidation of the femur. The study will allow to evaluate the performance of new devices required for the control of the surgery.

  • PhD student : Sohrab Rezai Lafmejani
  • Supervisors: M. Ehlinger, M. Bilasse, Y. Othmani

 

 

Tumour Segmentation in Multimodal 3D Imagery using Self-Supervised Learning

 

Manually analysing histological slices is the current gold standard in pathology. However, the
approach is complex, requiring sample preparation and manual slice screening. Through the use of
unsupervised deep learning to enable pathologists to easily analyse data, this project will enable
the use of new preparation-free 3D imaging techniques to produce medical input at a significantly
higher rate, supporting surgeons for intra-operative and per-operative decisions.

  • PhD Student: Laetitia Rebiere
  • Supervisors: T. Lampert, A. Venkatasamy

The project focuses on AI-assisted needle interventions using the new articulated « ARC needle », to develop methods dedicated to flexible needles and non-linear trajectories. It aims to facilitate their use by operators, promote their deployment and adoption in hospitals, and ultimately pave the way for robotic-assisted insertion to improve efficacy and performance. It will cover both automated preoperative planning of one or more articulated needle trajectories, and intraoperative trajectory correction.

  • PhD student: A. Morin
  • Supervisor: C. Essert, L. Rubbert, J. Verde

 

Laser scanning out of multi-core optical fiber for tumor treatment

Laser is a high potential tool to treat cancer by endoscopy. However, the laser spot positioning is a big issue. The mechanical systems used to move the endoscope distal end or the optical fiber are
not enough accurate. We propose to control the phase of a laser at the entrance of a multicore optical fiber to manipulate the laser spot inside the body using a computed generated hologram.

  • PhD Student: Stefan Jensen
  • Supervisors: Sylvain Lecler (INSA) & Michele Diana (IRCAD)