The face is the gateway to a person's soul. Even the slightest deformity of facial features outside the range perceived to be 'normal' can cause sociopsychological problems. Children born with severe cranofacial defects suffer much more and need to undergo many operations to correct breathing, hearing, eating and speech function as well as additional procedures to reconstruct contours of the face. To make treatment even more challenging, these syndromes express themselves in varying grades of severity in each patient and in each tissue type, and thus a unique and individualized reconstruction must be planned for each child.
Due to the growth of the face and its complex contours, the procedures to treat these craniofacial syndromes are amongst the most challenging in reconstructive surgery. The surgeries currently rely on the harvest of the patient’s fascial flaps, bone, muscle, fat and skin for reconstruction which poses additional burden on the child.
We propose a unique and high-technology solution for the design and fabrication of individualized craniofacial grafts for children born with craniofacial defects, thus eliminating some of the need for tissue harvesting. The goals of this project are:
1. The development of a statistical shape model which encompasses the naturally occurring variation in facial anatomy and bone structure of healthy, growing children.
2. The design of 3D models to restore tissue structures in patients with craniofacial defects
3. The advanced manufacture of hybrid which are texturally realistic, biocompatible, and mechanically robust
4. The pre-clinical evaluation of these grafts.
We will develop a toolbox of software and biofabrication processing methods and materials which could one day be used to treat children afflicted by these serious craniofacial conditions. These methods may can also be used for patient’s whose face has been deformed by cancer or trauma. An innovative feature of this project is the collaboration between computer scientists, bioengineers, medical doctors and entrepreneurs.
Was ist das Besondere an diesem Projekt?
The goals of the project are completely unique. Up until now the only treatment possible for people with severe facial malformations are fabrication of silicone prosthesis held to the face through magnetic pins. These implants are never accepted by the wearer as part of their body, but only as a foreign object. Creativity is required in designing the missing or underdeveloped features to enhance the overall aesthetics of the face. Creativity is also required to come up with new combinations of biomaterials (hybrid implants) to fulfil the complex requirements of the surgical site. This project combines aesthetics, computer science, material science and processing to achieve innovative medical products. Technically, modelling face surface and soft tissue under the constraint of the skull development of growing children has never been reported and the development of a detailed model of a growing child's face and skull is one of the truly innovative aspects of the the project. Our recent developments in computational anatomy are promising that the model developed will enable completely novel methods for a personalized implant design, These tools will aid immensely in the work of reconstructive surgeons to predict outcome of surgical procedures and how the face will look as it grows.
Zenobi-Wong laboratory for Tissue Engineering and Biofabrication is unique in that it combines expertise from cartilage cell biology, biomaterials synthesis and characterization, and biofabrication techniques including bioprinting, electrospinning and two-photon patterning. This team possesses both the depth and breadth in cartilage tissue engineering which is unique world-wide.
The Vetter lab for Computer Graphics and Computer Vision pioneered the development and application of 3D statistical shape models of the human face. Over the years the methodology was extended to bones of the human skeleton including models of the skull as well as to soft tissue and inner organs. The approach taken is unique by combining machine learning technologies with geometric modeling. The method is capable to model the range of normal appearance, to detect pathologies as well as to predict the shape of missing parts.
Markov Chain Monte Carlo for Automated Face Image Analysis
, Sandro Schönborn, Bernhard Egger, Andreas Morel-Forster and Thomas Vetter, International Journal of Computer Vision 123(2), 160-183 , 2017;
Kesti, M, C. Eberhardt, Pagliccia, G, Grande, D, Boss, D, Zenobi-Wong, M, Bioprinting complex cartilaginous structures with clinically-compliant biomaterials, Adv Funct Mat, 25: 7406-7417, 2015.
None so far
Am Projekt beteiligte Personen
Prof. Marcy Zenobi-Wong
, project leader, Dept Health Sciences & Technology, ETH Zürich
Prof Thomas Vetter, Dep. Mathematics and Computer Science, University of Basel
Dr. Ghazi Bouabene, Dep. Mathematics and Computer Science, University of Basel
Dr. Pierre Guillon, Dept Health Sciences & Technology, ETH Zürich
Prof Christian Kellenberger, Head of Radiology, Zürich Children's Hospital
Enrico Tosoratti, ETH Zürich
Letzte Aktualisierung dieser Projektdarstellung 17.10.2018