Model-based imaging and image-based modelling

My talk will update on three current research topics. 1.
Microstructure imaging, which uses mathematical or computational
modelling and machine learning to estimate and map microstructural
features of tissue. Examples from my group's work include NODDI (Zhang
et al Neuroimage 2012) for brain imaging and VERDICT (Panagiotaki et
al Cancer Research 2014) for cancer imaging. I will describe moves
towards a new paradigm combining multi-contrast measurements through
sophisticated computational models exploiting machine learning. 2.
Data-driven disease progression models (e.g. Fonteijn et al Neuroimage
2012; Young et al Brain 2014; Lorenzi et al Neuroimage 2017), which
aim to piece together longitudinal pictures of disease from
cross-sectional or short-term longitudinal data sets and thus gain
disease understanding, stratification systems, and predictive
power. The recent Subtype and Stage Inference (SuStaIn - Young et al
Nature Comms 2018) algorithm extends the idea to identify disease
subgroups defined by distinct longitudinal trajectories of change. 3
Image Quality Transfer (Alexander et al Neuroimage 2017; Tanno et al
MICCAI 2017), which uses machine learning to estimate a high quality
image, e.g. that we would have acquired from a one-off super-powered
scanner, from a lower quality image, e.g. acquired on a standard
hospital scanner.