JADS PhD Scholarship in Machine Learning for motion robust MRI of the bowels
Studentship: Untaxed bursary of £17,285 per annum (2020/21 figure including London weighting plus home/EU fees)
The Department of Computing is a leading department of Computer Science among UK Universities, and has consistently been awarded the highest research rating. In the 2014 REF assessment, The Department was ranked third (1st in the Research Intensity table published by The Times Higher), and was rated as “Excellent” in the previous national assessment of teaching quality.
Applications are invited for a PhD student in Machine Learning for motion robust MRI of the bowels under the supervision of Dr Bernhard Kainz (Imperial) and Dr Dimitrios Karampinos (TU Munich).
The PhD research will explore image reconstruction from sparse data using generative machine learning models in the context of Gastrointestinal tract Magnetic Resonance Imaging (MRI). Gastrointestinal tract MRI has superior soft tissue contrast to CT but suffers from limited resolution and artifacts due to involuntary gastrointestinal movements and respiration. Nevertheless, quantitative MRI techniques based on relaxometry mapping have shown great successes for tumor staging and therapy outcome assessment. Current quantitative abdominal MRI protocols rely on 2D acquisitions performed during breath-holds or using traditional respiratory motion synchronization, e.g. gated image acquisition driven by external sensors. Abdominal physiological motion is complex. Traditional gating techniques are not sufficient to achieve robust image quality across patients and can lead to prolonged inefficient scans for quantitative imaging. MoCoQ aims to develop contributions on two distinct levels: 1) Aim A (at TU Munich), microscopic signal generation including machine learning-enhanced self-navigated data acquisition and physiological monitoring techniques and 2) AIM B (this project at Imperial), macroscopic image reconstruction using generative machine learning models, in order to reconstruct plausible high-resolution and motion-free abdominal MR images at a quality level that competes with CT.
The research is at the intersection of artificial intelligence and healthcare and has the potential to make significant positive impact on society by improving patient care through better diagnosis and treatment.
Throughout the PhD there will be opportunities to spent time at TU Munich, Germany and the student will be able to participate in tailored AI training through the AI4Health CDT.
To apply for this position, you will need to have a strong background in at least one of the following areas: machine learning, computer vision, image computing, applied mathematics.
Applicants are expected to have a First Class or Distinction Masters level degree, or equivalent, in a relevant scientific or technical discipline, such as computer science or mathematics. Applicants must be fluent in spoken and written English.
The position is fully funded, covering tuition fees, travel funds and a stipend/bursary. The position is available to home and EU students.
How to apply
To apply for this position, please follow the application guidelines.
In the application form, please write JADS-AI4HEALTH in the “Proposed Research Topic” field, and Dr Bernhard Kainz in the “Proposed Research Supervisor” field.
Early applications are encouraged. Informal inquiries about this position are also encouraged and can be directed to Dr Bernhard Kainz (firstname.lastname@example.org). For further information about research see our projects pages.
This position will be based at the South Kensington campus in central London.
Applicants are advised to visit our PhD page for general information on becoming a PhD student in the Department of Computing.
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