Summer Scholarships
Each year Image X Institute offers a Summer Research Program, running across January and February
These scholarships provide an opportunity for students studying at the University of Sydney to undertake a summer vacation research project with investigators in our research program.
Especially suited to students with a background in subjects including (but not limited) to:
- Physics
- Mathematics
- Computer science
- Biomedical engineering
- Electrical engineering
Image X prides itself on offering a summer research program filled with opportunities. Students can work onsite amongst our multidisciplinary team, learning about the different projects and careers within our group. We host social activities, tours of our research facilities, as well as an introductory seminar series about radiation therapy and cancer imaging, presented by our star researchers. Many summer students continue working with Image X after their projects, from taking on part time research positions through to commencing PhD projects.
Successful applicants will be invited to choose from the projects below (please note, more projects will be added in the next month). Scholarships will be awarded based on the student’s academic transcript, relevant academic background or any research experience.
Applications will open on September 16th 2024 and close on October 13th 2024.
Include a short CV, academic transcript and statement on your experience / interest in research. See here for terms and conditions.
Apply here
Current Projects:
More projects will be added in September/October
First evaluation of AI-guided tumour and organ-at-risk tracking on lung cancer clinical trial data
Supervisors: Dr Nicholas Hindley, Dr Chandrima Sengupta
Project description:
Lung cancer is the leading cause of cancer-related death and the fifth most commonly diagnosed cancer in Australia. 77% of lung cancer patients will benefit from radiotherapy and hypofractionated treatments, where high doses are delivered in concentrated treatment sessions, have been shown to improve overall survival with 10 times fewer hospital visits. However, 1 in 2 lung cancer patients are ineligible for these highly effective treatments, because they suffer from metastases and we currently cannot adapt hypofractionated treatments to multiple independently-moving targets as patients breathe.
To maximise access to the most effective treatments while minimising radiation-induced toxicities, we invented Voxelmap, an open-source artificial intelligence system that tracks tumours and organs-at-risk during lung cancer radiotherapy (https://github.com/Image-X-Institute/Voxelmap). So far we have published results on the performance of Voxelmap using computer-generated data. In this exciting project, we will validate AI-guided tumour and organ-at-risk tracking on clinical trial data for the first time.
Given the aims of the project, experience with Python/MATLAB coding is a must and familiarity with machine learning frameworks (such as Pytorch) is a definite bonus! Having said that, we are willing to coach a motivated student.
Deep-learning based tumour motion monitoring for lung SABR
Supervisors: Dr Chandrima Sengupta, Dr Nicholas Hindley, Prof Paul Keall
Project description:
During radiotherapy, lung tumours move significantly from the planned treatment position. As a result, the radiation beam may miss the tumour and hit nearby healthy tissues, resulting in poor treatment outcomes.
This project will develop and retrospectively evaluate a deep-learning based lung tumour tracking algorithm in x-ray images acquired in three clinical trials in Australia. Conditional Generative Adversarial Networks (cGANs) will be used for image segmentation. The method developed during this project will be investigated through a real-time framework for its clinical implementation.
Developing a surface tracking method for cardiac radioablation treatments
Supervisors: Alicja Kaczynska, Dr Chandrima Sengupta, Prof Paul Keall, Dr Youssef Ben Bouchta
Project description:
Noninvasive cardiac radioablation with focused, high doses of radiation is a promising alternative treatment for ventricular tachycardia, a disease of abnormal heartbeat which is one of the most common causes of sudden cardiac death. Real-time tracking of complex cardio-respiratory motion is crucial to ensure the radiation is accurately delivered to the beating heart without delivering extra radiation dose to surrounding healthy tissues.
This project will develop and retrospectively evaluate a surface tracking method to monitor chest and abdomen motion during cardiac radioablation using preclinical data from an ethics approved study. The method will involve the use of a surface image segmentation tool, which will be combined with computer vision software that the student will develop to track the signal from the segmented surface. The development of this surface tracking method will provide a foundation for the development of motion tracking technologies for cardiac radioablation.
Optimisation of deep learning networks for real-time tumour tracking for pancreas SABR.
Supervisors: Dr Chandrima Sengupta, Adam Mylonas, Prof Paul Keall.
Project description:
During radiotherapy, pancreas tumours move significantly from the planned treatment position. As a result, the radiation beam may miss the tumour and hit nearby healthy tissues, resulting in poor treatment outcomes.
To perform pancreas tumour tracking in x-ray images, we have developed Conditional Generative Adversarial Network (cGAN) based segmentation method. However, the soft tissue contrast in the pancreas is poor, making the segmentation challenging. This project will optimise the performance of the cGAN network for its real-time implementation into clinical practice. The performance of the network will be retrospectively evaluated against implanted markers/other landmarks.
Parahydrogen hyperpolarized nuclei for MRI-guided radiotherapy
Supervisors: Dr Thomas Boele, Dr David Waddington
Project description:
Better diagnostic imaging and biologically targeted radiotherapy can improve cancer patient outcomes. Magnetic resonance imaging (MRI) provides high resolution images with excellent soft tissue contrast for clinical diagnosis. MRI-guided radiotherapy has emerged as a clinical tool in the last decade as treatment devices combining imaging and targeting have become available to oncology departments.
However, MRI is limited by its low sensitivity, inherent to the technique’s reliance on the small net magnetic moment of ensembles of nuclear spins in biological systems. Hyperpolarization techniques can boost the available MR signal by greater than four orders of magnitude to enable imaging modalities that are impractical or impossible with traditional methods.
In this project we aim to build hardware to develop a flexible and low-cost approach based on the transfer of quantum spin order from parahydrogen to the nuclei of interest to enhance access to cutting edge hyperpolarization technology. There are multiple directions of research available for a student interested in exploring the intersection between engineering, quantum physics and medical imaging.
The impact of low dose CT on CT ventilation image quality in the context of lung cancer screening programs
Supervisors: Prof Paul Keall, Dr Hunor Kertesz, Jeremy Lim
Project description:
Computed tomography ventilation imaging (CTVI) is a fast growing clinical modality. CTVI has been applied across respiratory diseases, including lung cancer, interstitial lung disease and idiopathic pulmonary fibrosis. The first CTVI product was FDA-approved in 2023.
Also, increasing are national lung cancer screening programs. The US National Lung Screening Trial found from over 50,000 persons at high risk for lung cancer that screening reduced mortality. Based on the findings, the US National Comprehensive Cancer Network (NCCN) guidelines recommended low dose CT for patients at high risk of lung cancer.
A feature of lung cancer screening programs is that, to balance the costs and benefits of CT imaging, low dose CT scans are used. These scans have increased noise compared with standard CT scans and may impact the quality and accuracy of the CTVI derived from the scans. However, to date, no dedicated studies investigating the impact of low dose CT on CTVI quality have been performed.
Therefore, to address the knowledge gap, the aim of this project is to quantify the impact of low dose CT on CTVI quality in the context of pairing CTVI with lung cancer screening programs.
Optimisation of a real-time tumour tracking framework
Supervisors: Dr Chandrima Sengupta, Freeman Jin, Dr Ben Zwan, Daniel Mason, Prof Paul Keall
Project Description:
Tumours in the thorax, abdomen and pelvis move during radiation therapy resulting in inaccurate treatment and poor treatment outcomes. At Image X, we have developed a series of real-time tumour tracking methods to overcome this problem.
This project will develop an optimisation framework to optimise the tracking parameters based on patient size and other clinically relevant parameters including dose rate, imaging parameters and breathing magnitude. The results will be evaluated on our 5M+ patient images acquired in multi-institutional clinical trials.
Improved Speech Imaging with Real-Time MRI
Supervisors: Dr David Waddington
Project Description:
Speech is a crucial aspect of human communication, yet there is still much to learn about how people shape their vocal tract during speech production. While MRI technology has the potential to provide detailed images of speech, current real-time MRI techniques are too slow to capture the rapid movements involved.
This project aims to utilize advanced reconstruction techniques to enhance the quality and speed of videos showing people speaking while in an MRI scanner. Students with a background in mathematics, engineering, physics, and coding (Python and/or MATLAB) will be well-suited for this project. By the end of the project, the student will produce a comparative analysis of various image reconstruction methods and a set of real-time videos showing human speech. This project is a collaboration with the Discipline of Speech Pathology and the School of Electrical and Computer Engineering.