  The imaging and subsequent accurate diagnosis of paediatric brain tumours
presents a radiological challenge, with magnetic resonance imaging playing a
key role in providing tumour specific imaging information. Diffusion weighted
and perfusion imaging are commonly used to aid the non invasive diagnosis of
paediatric brain tumours, but are usually evaluated by expert qualitative
review. Quantitative studies are mainly single centre and single modality. The
aim of this work was to combine multi centre diffusion and perfusion imaging,
with machine learning, to develop machine learning based classifiers to
discriminate between three common paediatric tumour types. The results show
that diffusion and perfusion weighted imaging of both the tumour and whole
brain provide significant features which differ between tumour types, and that
combining these features gives the optimal machine learning classifier with
greater than 80 percent predictive precision. This work represents a step
forward to aid in the non invasive diagnosis of paediatric brain tumours, using
advanced clinical imaging.
