CDT Induction & Mres Best Student Award 2017
We would like to welcome new and returning students that are settling back in to the first weeks of the new academic year.
On Tuesday 17 October 2017, the first in a series of evening seminars took place featuring Dr Shonit Punwani who spoke about his team’s recent success performing the first hyperpolarised MRI scan in Europe of a patient with prostate cancer. The scan was part of a UCL clinical trial aimed at offering more accurate and personalised treatment for cancer patients. Read more about this trial here.
This talk was preceded by an induction for the students. This began with a welcome from Centre Director, Prof Sebastien Ourselin and, following this, the MRes Student Award for the academic year 2017 was presented.
The 3 students with the highest overall marks were shortlisted for this award, then these final students were assessed on the quality of their research project report and the quality of their project presentation. All shortlisted students gave short presentations on Tuesday about their research, highlighting its significance and progress.
The runners up were Natalie Holroyd, ‘Towards Multi-fluorescence HREM: a High-Resolution Modality for Probing the Tumour Microenvironment’ and Savvas Savvidis, ‘Advanced Imaging Techniques for the Monitoring of Innovative Approaches in Regenerative Medicine’.
The winner of the award was Benjamin Davidson for his project on ‘Multi-scale Multi-modal Retinal Image Fusion: From Neurons to Vessels’. Early retinal disease can be indicated by the number and arrangement of cones on the eye’s photoreceptor layer. This mosaic of tiny cone photoreceptors can be measured by using an imaging modality called adaptive optics scanning light ophthalmoscopy (AOSLO) that produces microscopic, high-resolution images of the eye.
However, whilst this is a crucial biomarker for the early detection of disease, counting the cones is a very time-consuming and labour-intensive process. A single data set can typically take a day and half of full-time work to manually analyse and quantify, limiting its potential clinical use.
Alongside other researchers from UCL Institute of Ophthalmology and Moorfields Eye Hospital, Ben’s project involved developing a machine learning algorithm that could automatically detect, quantify and classify the photoreceptors instead of relying on a human expert. Ben talked through this process during his presentation and how his research has advanced to enable detection that is both robust and fast. We look forward to seeing how he continues to progress this research throughout his PhD years and the potential impact it has to enable the early diagnosis and treatment of retinal diseases for patients.