CENTER PHDS

Mr. Duy Quang Pham 

Project
Bioactive ceramic coatings with antimicrobial properties to increase orthopaedic implant longevity

Description
The short lifespan of orthopaedic implants is a major clinical problem, where failure often occurs within a few months because of infection, or within 10–15 years due to loosening. Most orthopaedic implants use titanium alloys (Ti-6Al-4V), which often cannot achieve sufficient integration with bone, and currently used hydroxyapatite coatings are prone to delamination and fragmentation. This doctoral thesis will develop a family of patented ceramics for use as novel implant coatings: Baghdadite (US patent 9,005,647), Sr-HT (US patent 8,765,163), and Sr-HT-Gahnite (US patent 9,220,806). It aims to develop a family of ceramic coatings for orthopaedic implants, featuring osteogenic and antimicrobial properties coupled with high bonding strength to prevent premature implant failure. We will optimise the plasma-spraying process for coating deposition to produce high bonding strength while ensuring that the inherent bioactivity and antimicrobial properties of the ceramics are retained. This will reduce the costs associated with revision surgery and greatly improve the recipients’ long-term quality of life.


 

Mr. Ben Ferguson

Supervisors:   Prof. Qing Li  and Prof. Hala Zreiqat

Project
Computational modelling and optimisation of a bone-scaffold fixation-plate biomechanical system

Description
Mr. Ferguson’s doctoral thesis involves the research and development of a novel, additively manufactured tissue scaffold-fixation system, which is used to reconstruct a jawbone with a large defect. The system is optimized through subject-specific numerical modelling methods and experimental tests.


 

Mr. Jett van der Wallen

Project
Implantable Biosensors to Monitor and Stimulate Tissue Regeneration

Description
The recording and utilisation of muscle tissue electrical activity, otherwise known as myoelectric signals, has considerable importance in the bioelectronics field. A myoelectric sensor is a device that records and transmits myoelectric signals, by interfacing with muscle tissue. This is the premise for their usage as control inputs, such as in prostheses, functional electrical stimulation (FES), and wearable and remote robotic devices. Myoelectric sensors are also used in diagnostics, to assess or diagnose neuromuscular performance, injuries and syndromes. Furthermore, they are utilised for kinesiology studies, such as gait analysis. Surface myoelectric sensors (SMES) and implantable myoelectric sensors (IMES) will be researched and developed, in affiliation with The University of Sydney and the ARC TCIBE. This will be achieved by utilising fastidious engineering approaches, such as 3D-printing and laser-cutting, to produce biocompatible and efficacious sensors. The myoelectric sensors will connect to external circuitry, for post-processing and analysis, to fulfill their intended bioelectronic application.


 

Project
Deep learning based radiomics framework for the analysis of Soft-tissue Sarcomas in omni-modality images

Description
The implementation and availability of high-throughput computing has made it possible to extract innumerable features from medical imaging datasets. These extracted features can reveal disease related characteristics that can relate to prognosis, for example, predicting the development of distant metastases or overall survival rate of patients. The process of converting visual imaging data into mineable quantitative features is referred to radiomics. Radiomics is an emerging field of translational research in medical imaging where the modalities include digital radiography, magnetic resonance imaging (MRI), computed tomography (CT), combined positron emission tomography – computed tomography (PET-CT) etc. We will propose a radiomics framework for the analysis of soft-tissue sarcomas in omni-modality images, using state-of-the-art convolutional neural networks – a data-driven approach to identify the quantifiable image characteristics that are most relevant for a particular task – in this case, patients’ outcome prediction. The key challenge will be to train the CNNs across all image types (both functional and anatomical) to identify the correlations between them so that prognosis related information can be learned regardless of the image type. Ultimately, our proposed framework could potentially improve both diagnostic and prognostic processes by allowing automated omni-modality information fusion for the analysis of soft-tissue sarcomas.


 

Ms. Queenie Yip

Supervisors: Prof. Hala ZreiqatProf. Patrick Tam, Dr. Peter Newman

Project
Development of advanced methods for applications in organoid technologies

Description
The development of organoid technologies in recent literature has engendered mass interest in its potential to be harnessed as developmental models. These studies have generated a wide range of organoid systems, including tissue of cortical, retinal, and hepatic origins. The prospective applications of these self-organised three-dimensional tissue cultures have been noteworthy including, but not limited by, live-cell imaging for enhancing the understanding of interactions between developmental signalling pathways, disease modelling to gain insight into certain phenotypes and improve the efficacy of drug therapeutics through models which more accurately simulate human physiology, and cell therapies for patients with certain functional deficiencies. Thus, further research into the field of these technologies, through advanced techniques such as micropatterned substrates and geometrically guided parameters, is warranted and may greatly enhance the current body of knowledge. These organoid models will offer significant insights into developmental processes as well as the potential for more robust therapeutic options.


 

Topic
Advanced 3D visualization of musculoskeletal (MSK) imaging

Description
Most modern musculoskeletal (MSK) imaging data are volumetric. For example, the shape, size and location of the anatomical structures and MSK defects are natively specified in 3D space. Although 3D visualization has a tremendous potential to improve the clinical diagnosis workflow, unfortunately, current MSK visualization techniques are dominated by 2D cross-sectional view because automatic 3D MSK visualization is a non-trivial task. This is an attribute to the fact that existing 3D MSK visualization methods require extensive manual tweaking to tune the visualization to depict the important features e.g., MSK defects. This manual tweaking is time-consuming and has a high learning curve. This project will produce a new 3D MSK visualization algorithm that enables clinicians to view the anatomical characteristics of the MSK defect with minimum user interactions and ultimately, enables improved diagnosis and presurgical planning.


 

Mr. Haobo Guo

Project
Fluorescent nano sensors for metal ions

Description
His research interests centre around establishing a multiplexing approach to selective metal sensing. Thesis, he wants to explore the distinguishing and quantifying of transition metals in biological system, due to the undiscovered metallobiology roles of these metals ions. To do so, he developed his research project from the following three phases. Firstly, to design and establish multiplexing sets of cross-reacting elements. The fluorescent metal ions sensing library will be developed. These sensing units are suitable for identifying and quantifying metals in solution. Secondly, to utilize metal elements sensing groups/compounds to functionalize the chemical scaffold. A variety of different scaffolds will be reviewed to determine the most stable structures for multiplexed sensors. Thirdly, to incorporate new multiplexed metal-sensing techniques into biological systems. This multiplexed metal-sensing system will be applied to solve cutting-edge biological research issues. 


 

Mr. Kingsley Won-ching Poon

Title
Next generation theranostic agent for MR imaging and therapy

Description
Theranostics is a new emerging field aiming to combine diagnostics with therapy in a single treatment. As healthcare costs continually climb, the ability to provide healthcare in a more efficient and effective manner is economically enticing. Nanoparticle-drug systems hold the key in developing these agents due to their versatility and unique chemical and physical properties. However, there is a significant gap in knowledge that prevents their transition to clinical use. Namely, there is a lack of systematic understanding of the material properties, its biological properties, and the interplay between these factors. We will develop a proof-of-concept novel theranostic agent for MR imaging and drug delivery, using a methodical approach to understand its in vivo behaviour. The primary focus will be on Parkinson’s disease.


 

Ms. Roshanak Elyasi

Title
Towards Ultra-low-power Seizure Forecasting Systems

Description
Miniaturized, multi-functional IMDs are growing in importance as they enable continuous monitoring, early-stage detection, and initial treatment of dysfunctional organs. The power requirement associated with some of these devices is, however, a significant challenge. The amount of power requirement, longevity, and size of the power supply, tissue safety dictate today’s specifications of modern IMDs.  The wireless power transfer (WPT) solutions can be defined as a technology capable of transmitting energy across a medium, from a power source to an electrical load, without electrical wires connecting this power source to the load. These techniques could use both electromagnetic (EM) and non-EM energy. This project aims to provide the best solution for powering a biomedical implant, precisely a neural implant applicable for seizure forecasting systems.

Moreover, the possibility of backward biotelemetry on the same link of delivered power should be investigated later. However, this would add another layer of complexity as some design specifications on power delivery systems conflict with higher bandwidth requirements for data telemetry.

Tel: 0061 2 9114 4607
Email: artcibe@sydney.edu.au

 

 

@2021 ARC Centre for Innovative BioEngineering

Level 4, J07
Faculty of Mechanical Engineering
University of Sydney
Darlington NSW 2008
Australia