Dr Matthew Grech-Sollars is an Associate Professor in Quantitative Neuroradiology at University College London and a Clinical Scientist at University College London Hospitals NHS Foundation Trust. He is also an Honorary Senior Lecturer at Imperial College London. He has a key interest in developing MRI tools for use within a clinical environment, with a particular focus on quantitative MR and 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.
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 then received his PhD from University College London in 2014, titled “Diffusion MRI for characterising childhood brain tumours” and supervised by Prof Chris A Clark. 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. During this time he supervised student projects and gave lectures in MRI Physics and Cancer Imaging to Masters students from across the Faculties of Medicine and Engineering.
et al., 2022, A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease, Communications Medicine, Vol:2, ISSN:2730-664X
et al., 2022, Intraoperative ultrasound in brain tumor surgery: A review and implementation guide, Neurosurgical Review, Vol:45, ISSN:0344-5607, Pages:1-13
et al., 2022, Temperature dependence, accuracy, and repeatability of T-1 and T-2 relaxation times for the ISMRM/NIST system phantom measured using MR fingerprinting, Magnetic Resonance in Medicine, Vol:87, ISSN:0740-3194, Pages:1446-1460
et al., 2021, Current applications and future development of magnetic resonance fingerprinting in diagnosis, characterization, and response monitoring in cancer, Cancers, Vol:13, ISSN:2072-6694, Pages:1-20
et al., 2020, Optimisation of deep learning methods for visualisation of tumour heterogeneity and brain tumour grading through digital pathology, Neuro-oncology Advances, Vol:2, ISSN:2632-2498
et al., 2019, Imaging and tissue biomarkers of choline metabolism in diffuse adult glioma; 18F-fluoromethylcholine PET/CT, magnetic resonance spectroscopy, and choline kinase α, Cancers, Vol:11, ISSN:2072-6694, Pages:1-15
et al., 2019, Reliability of dynamic contrast enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models, Neuroradiology, Vol:61, ISSN:0028-3940, Pages:1375-1386
et al., 2018, Stability and reproducibility of co-electrospun brain-mimicking phantoms for quality assurance of diffusion MRI sequences, Neuroimage, Vol:181, ISSN:1053-8119, Pages:395-402
et al., 2017, An MRS- and PET-guided biopsy tool for intraoperative neuronavigational systems., J Neurosurg, Vol:127, Pages:812-818
King MD, Grech-Sollars M, 2016, A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo (MCMC) simulation, F1000 Research, Vol:5, ISSN:2046-1402
et al., 2015, Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain., NMR in Biomedicine, Vol:28, ISSN:0952-3480, Pages:468-485
et al., 2014, Challenges for the functional diffusion map in pediatric brain tumors, Neuro-Oncology, Vol:16, ISSN:1522-8517, Pages:449-456
et al., 2012, Survival analysis for apparent diffusion coefficient measures in children with embryonal brain tumours, Neuro-Oncology, Vol:14, ISSN:1522-8517, Pages:1285-1293
Bangerter NK, Morrell G, Grech-Sollars M, 2020, Magnetic resonance imaging, BIOENGINEERING INNOVATIVE SOLUTIONS FOR CANCER, Editor(s): Ladame, Chang, ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD, Pages:163-194, ISBN:978-0-12-813886-1