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Machine Learning for Computational Medical Imaging

  • Full or part time
  • Application Deadline
    Monday, April 01, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

With recent advances in machine learning, we are witnessing a new wave of data-driven approaches emerge to uncover the physical models behind many complex systems in modern applications. In this project, we particularly aim at developing machine learning techniques for the Quantitative Magnetic Resonance Imaging (Q-MRI). In Q-MRI rather than simply forming a single MR image with only contrast information (i.e. qualitative MRI), physicists are interested in uncovering the NMR properties of tissues such as the relaxation times and proton density from a time-series of images. The main challenge of standard Q-MRI approaches is their very long acquisition time. Recently, MR Fingerprinting (MRF) emerged to address this issue using combined tools from Compressed Sensing theory and pattern recognition. The MRF framework simulates offline a dictionary of fingerprints (i.e. magnetic responses) for all NMR parameters and searches through this dictionary to track back the underlying parameters from the measurements. However this approach is known to be non-scalable and bottlenecked by the heavy storage and computation requirements of searching through very large dictionaries in multi-parametric Q-MRI applications.

Combined with recent advances in Deep Learning models, this project aims at building dictionary-free systems to address the non-scalability issue of the MRF problem. This requires designing a new neural network architecture and an efficient training strategy to learn complex physical dynamics behind Q-MRI. This project will run in close collaboration with one of the world-leading medical imaging industries and the proposed methodologies will be validated against real-world medical imaging datasets.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found at: http://www.bath.ac.uk/research-centres/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree. A master’s level qualification would also be advantageous.

Informal enquiries about the project should be directed to Dr Mohammad Golbabaee: .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013

Start date: 23 September 2019.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

How good is research at University of Bath in Computer Science and Informatics?

FTE Category A staff submitted: 24.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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