Microstructure imaging with MRI, data-driven disease progression modelling, and other topics at UCL-CMIC

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• Danny Alexander, UCL
• Friday 02 June 2017, 16:00-17:00
• MR13 Centre for Mathematical Sciences.

If you have a question about this talk, please contact Rachel Furner.

Joint CMIH-CCIMI seminar.

Daniel Alexander is Professor of Imaging Science in the Department of Computer Science at UCL and Director of UCL ’s Centre for Medical Image Computing (CMIC: cmic.cs.ucl.ac.uk). His background is in Mathematics and Computer Science. His research interests are in medical imaging and computational modelling for healthcare applications. Primary application areas are in neurological disease and cancer. More information here: www.cs.ucl.ac.uk/staff/ucacdxa.

I’ll give a brief overview of work at CMIC . Then my talk will focus on the work of the Microstructure Imaging Group (MIG: mig.cs.ucl.ac.uk) and the Progression of Neurological Disease (POND: cmic.cs.ucl.ac.uk/pond) initiative within CMIC . Microstructure imaging uses computational modelling and machine learning to gain biological specificity in MRI by targetting features of tissue microstructure normally only accessible through invasive histology. Examples that have recently made the leap to clinical utility include NODDI (Zhang et al Neuroimage 2012) for brain imaging and VERDICT (Panagiotaki et al Cancer Research 2014) for cancer imaging; I will describe the current state of the art and future potential. Data-driven disease progression models aim to combine multi-modal measurements from patients into holistic models of disease that support early diagnosis, fine-grained staging, and subtyping for refined prognosis and patient stratification. I will describe our work on the event-based model (Fonteijn et al Neuroimage 2012; Young et al Brain 2014) as well as a range of more sophisticated models currently in development within the EuroPOND project (www.europond.eu). I’ll also mention briefly recent work on Image Quality Transfer (Alexander et al Neuroimage 2017) using machine learning propagate information from high quality images, e.g. from a specialist scanner, to lower quality but cheap or widely available images, e.g. from clinical systems.

This talk is part of the CMIH Hub seminar series series.

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