Study level

  • Master of Philosophy
  • Honours

Faculty/School

Faculty of Science

School of Chemistry and Physics

Topic status

We're looking for students to study this topic.

Research centre

Supervisors

Dr Konstantin Momot
Position
Senior Lecturer in Experimental Physics
Division / Faculty
Faculty of Science
Professor Rik Thompson
Position
Professor of Breast Cancer Research
Division / Faculty
Faculty of Health

External supervisors

  • Dr Jason Dowling, CSIRO Medical Imaging Division
  • Dr Thomas Lloyd, Princess Alexandra Hospital, Division of Radiology

Overview

The aim of this project is to develop accurate low-cost medical imaging methodology for pseudo-3D mapping of Mammographic Density (MD) within the breast.  MD is the degree of radio-opacity (“whiteness”) in an X-ray mammogram. It has implications for breast cancer risk, ease of detection of breast cancer, and monitoring of the efficacy of hormonal breast cancer prevention or anti-cancer treatments.

Healthcare Challenge

There is a growing need for affordable and accurate quantitative assessment of MD without ionising radiation. Magnetic resonance imaging (MRI) is capable of mapping the 3D distribution of MD within the breast as well as identifying benign and malignant abnormalities. However, cost is a major impediment to the use of MRI for routine MD assessment.

Our research group has pioneered biomedical applications of portable single-sided nuclear magnetic resonance (“portable NMR”; pNMR) for breast tissue imaging. pNMR is a low-cost, mobile technology based on the same fundamental physics as conventional MRI. Our results demonstrate the promise of pNMR for accurate, safe and low-cost quantitative assessment of MD. This project aims to translate this promise into a new robust clinical imaging technique for safe and low-cost quantitative assessment of MD in vivo.

Research activities

In its basic form pNMR is a depth-profiling (“1D imaging”) technique. In this project you will develop the methodology for acquiring pseudo-3D maps of MD within the breast by combining pNMR depth profiles measured at different topographical locations and angles. The aim is to reconstruct information comparable to conventional 3D clinical MRI, but from a low-cost 1D imaging technique. You will also optimise the acquisition strategies and reconstruction of pseudo-3D pNMR images to make them more robust and less susceptible to distortions associated with sensor positioning or breast compression.  The work will initially be carried out on custom-made anthropomorphic phantoms emulating the 3D distribution of MD within the breast that will be manufactured from 3D-printed scaffolds and lipid-water gels. You will also work with our collaborators at Princess Alexandra Hospital to apply the pNMR protocols developed to clinical imaging in patients undergoing hormone replacement therapy or anticancer tamoxifen adjuvant therapy.

Outcomes

A successful project will contribute to the development of novel approaches to medical imaging. The low cost of pNMR, its non-invasiveness, safety and suitability for deployment outside of hospital environment potentially make it the ideal platform for mobile clinical imaging tools. The approaches developed in this project will pave the way for low-cost Portable NMR instrumentation to be used for a broad range of quantitative medical imaging, expanding the availability of medical imaging in remote and rural locations, in primary emergency care, and for routine low-cost screening.

In Breast Imaging applications, the ability to quantitatively measure MD without ionising radiation will be instrumental in monitoring MD response to hormonal therapy, which could significantly improve the targeting of these treatments by identifying non-responders early in the treatment course and/or lower effective doses on a personalised basis. pNMR also has the capacity to become a screening methodology for regular assessment of MD in the general population, especially in younger women, measuring the effects of lifestyle factors on MD, and/or quantifying the contribution of high MD to breast cancer risk.

Skills and experience

To be considered for this project, you should have:

  • a strong interest in medical imaging and medical physics 
  • strong mathematical skills 
  • well-developed scientific programming skills.

The following skills will be an advantage:

  • familiarity with one or more of: Mathematica, Matlab, Python 
  • good understanding of the physical principles of Magnetic Resonance Imaging (MRI) 
  • practical experience with 3D printing 
  • knowledge of basic chemistry.

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

Contact

E-mail k.momot@qut.edu.au for more information.