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Dr Matthew Grech Sollars
90 High Holborn
London
Dr Matthew Grech Sollars profile picture
Appointment
  • Associate Professor in Quantitative Neuroradiology
  • Dept of Computer Science
  • Faculty of Engineering Science
Biography

Dr Matthew Grech-Sollars is an Associate Professor in Quantitative Neuroradiology at University College London and a Clinical Scientist at the National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust. Matthew graduated in Electrical Engineering from the University of Malta in 2005 and after working in industry, he pursued an MSc in Biomedical Engineering with Medical Physics at Imperial College London in 2008-2009. He received his PhD in Medical Physics from University College London in 2014. Matthew then joined Imperial College London as an MRI Physicist, while training as a Clinical Scientist at Imperial College Healthcare NHS Trust. In 2017, he was awarded an Imperial College Research Fellowship, which he carried out until 2021. Prior to joining UCL, Matthew was Principal MRI Physicist at Royal Surrey NHS Foundation Trust, while carrying on with his research activities at Imperial College London and in collaboration with the National Physical Laboratory and the University of Surrey.

Research Summary

Dr Matthew Grech-Sollars has a key interest in developing MRI tools for use within a clinical environment, with a particular focus on quantitative MR and neuro-oncology. As a Clinical Scientist, he bridges the clinical and non-clinical sciences, and is leading on the development of MR Fingerprinting for the assessment of brain tumours in collaboration with Siemens Healthineers. Matthew has extensive experience of working on multi-centre trials and the implementation of advanced imaging techniques in a clinical environment; including diffusion MR, perfusion MR and MR spectroscopy, as well as experience in PET-MR techniques. He also has an interest in developing AI tools to aid clinicians and improve patient outcomes, and in particular in implementing explainable AI systems for the development of such tools.

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