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SUMMARY:Microstructure imaging with MRI\, data-driven disease progression 
 modelling\, and other topics at UCL-CMIC - Danny Alexander\, UCL
DTSTART:20170602T150000Z
DTEND:20170602T160000Z
UID:TALK72617@talks.cam.ac.uk
CONTACT:Rachel Furner
DESCRIPTION:\nDaniel Alexander is Professor of Imaging Science in the Depa
 rtment 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 Mathemati
 cs 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.\n  \nI’ll give a brief overview of work at 
 CMIC. Then my talk will focus on the work of the Microstructure Imaging Gr
 oup (MIG: mig.cs.ucl.ac.uk) and the Progression of Neurological Disease (P
 OND: cmic.cs.ucl.ac.uk/pond) initiative within CMIC.  Microstructure imagi
 ng uses computational modelling and machine learning to gain biological sp
 ecificity in MRI by targetting features of tissue microstructure normally 
 only accessible through invasive histology. Examples that have recently ma
 de the leap to clinical utility include NODDI (Zhang et al Neuroimage 2012
 ) for brain imaging and VERDICT (Panagiotaki et al Cancer Research 2014) f
 or cancer imaging\; I will describe the current state of the art and futur
 e potential.  Data-driven disease progression models aim to combine multi-
 modal measurements from patients into holistic models of disease that supp
 ort early diagnosis\, fine-grained staging\, and subtyping for refined pro
 gnosis and patient stratification.  I will describe our work on the event-
 based model (Fonteijn et al Neuroimage 2012\; Young et al Brain 2014) as w
 ell as a range of more sophisticated models currently in development withi
 n the EuroPOND project (www.europond.eu). I’ll also mention briefly rece
 nt work on Image Quality Transfer (Alexander et al Neuroimage 2017) using 
 machine learning propagate information from high quality images\, e.g. fro
 m a specialist scanner\, to lower quality but cheap or widely available im
 ages\, e.g. from clinical systems.
LOCATION:MR13 Centre for Mathematical Sciences
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