Study level

  • Master of Philosophy

Faculty/School

Faculty of Engineering

School of Mechanical, Medical and Process Engineering

Topic status

We're looking for students to study this topic.

Research centre

Centre for Biomedical Technologies

Centre for Biomedical Technologies

Supervisors

Dr Jacqui Mcgovern
Position
Senior Lecturer (CGRA)
Division / Faculty
Faculty of Health
Dr Flavia Medeiros Savi
Position
NHMRC Postdoctoral Research Fellow
Division / Faculty
Faculty of Engineering

Overview

Biodegradable scaffolds are increasingly used to treat critical-sized bone defects, yet their in vivo degradation kinetics remain poorly understood. Existing analyses rely on limited morphometric measurements and lack automated computational tools. This project will use a unique longitudinal micro-CT dataset to characterize scaffold degradation over time, providing quantitative insights to support the design of next-generation biodegradable orthopedic implants.

Research activities

Develop an automated computational framework to quantify the in vivo degradation kinetics of biodegradable medical-grade polycaprolactone (mPCL) scaffolds using longitudinal micro-CT datasets. Create image analysis algorithms and mathematical models to measure and predict changes in scaffold morphology, volume, and strut geometry over time, and compare degradation behavior across 3 cm and 6 cm critical-sized bone defects.

Outcomes

This project aims to:

  • develop an automated computational workflow for quantitative analysis of scaffold morphology from longitudinal micro-CT datasets
  • characterise the temporal evolution of scaffold degradation using longitudinal computational image analysis
  • develop predictive mathematical models describing the in vivodegradation kinetics of biodegradable scaffolds.

The expected outcomes are:

  • A validated automated image analysis platform capable of extracting quantitative scaffold morphology from longitudinal micro-CT images
  • A comprehensive longitudinal dataset describing the degradation behavior of biodegradable scaffolds under different implantation conditions
  • a predictive computational framework capable of quantifying and forecasting in vivo scaffold degradation kinetics across multiple implantation time points and defect sizes.

Skills and experience

  • Computational image analysis
  • Computer vision
  • Biomedical image processing
  • Mathematical modelling
  • Artificial intelligence and machine learning (optional)
  • Three-dimensional image reconstruction
  • Micro-computed tomography (micro-CT can be trained)
  • Data science and scientific programming
  • Quantitative analysis

Scholarships

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Keywords

Contact

Contact the supervisor for more information