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Abstract #0012

Unsupervised neural networks to improve quantitative DCE modelling

Oliver Gurney-Champion1, Matthew Orton1, Kevin Harrington1, Uwe Oelfke1, and Sebastiano Barbieri2
1The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom, 2Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia

We introduce a novel approach to fitting parameters from DCE MRI using an unsupervised neural network. The network is trained on in vivo data, with no ground truth, and is able to predicts DCE model parameters directly from the obtained MRI images. In simulations, our method outperformed the ordinary least squares fit approach in that it is more accurate and precise. In vivo, it produced substantially less noisy parameter maps than the current practise least-squares fit.

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