Photon Factory

Project

This project focuses on assessing the performance of our photonic probes in animal prostate tissue. The project includes experimental work (design experiment, data collection) and data processing with a range of software we have developed (chemometrics techniques).

The fibre-based photonic probe uses Raman spectroscopy to identify healthy and malignant tissue. More specifically, the probe is based on a technique called Spatially Offset Raman Spectroscopy (SORS) which enables measurements on the surface and in the depth of the tissue. However, each probe design has its limitations (i.e. how deep can it measure).

This project will help understanding the maximum measurement depth in a range of biological samples.

Background
The current standard treatment for patients with prostate cancer is removal of the prostate gland (prostatectomy). Evaluation of surgical margins during these radical prostatectomy procedures remains a significant challenge. Too often (up to 38% of cases) malignant tissue is left behind leading to cancer spreading again and thus significantly lowering the chances of surviving the disease.

We are developing a photonic probe capable of detecting prostate cancer in real-time. This probe will help surgeons during prostatectomy procedures to decide if they need to remove more tissue. Thus, the probe has the potential to significantly change current medical practice.

Key words
Biosensing, Photonics, Laser, Raman spectroscopy, Fibre laser, Chemometrics, Data analysis.

Note: The project can be extended to a Honours or Master project.

Project

Raman spectroscopy can provide powerful biochemical fingerprints of disease such as cancer. This project focuses on the development and optimization of the data processing algorithms used to process the spectroscopy(Raman) data we obtained during a clinical trial on humans.

The project will explore:

• Diagnostic performance as a function of the tissue origin within the prostate
• Advanced Chemometric analysis methods to resolve key differences between healthy and unhealthy prostate tissue
• Advanced computing methods (AI or machine learning) to improve diagnosis ability

This project sits across multiple disciplines (Physics, Chemistry, Biophysics, advanced computing, and Chemometrics). We already have the data, so the work to be done is more computer-based: data processing and coding (Python or Matlab) and data analysis (chemometrics).

Project

Students with an interest in optical spectroscopy, coding and condensed matter research are encouraged to apply for this project.

This project will include the participation in the design of a Second Harmonic Generation Spectroscopy, and studying a series of condensed material systems such as superconductors and ferroelectrics.