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Abstract #0335

Accelerating perfusion quantification using ASL-MRI with a neural network based forward model

Yechuan Zhang1 and Michael A Chappell1,2,3
1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, 2Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 3Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

Arterial Spin Labelling is established as a quantitative technique to measure perfusion and other hemodynamic properties of the cerebral vasculature. This application of ASL requires multiple post label delays and parameter estimation via a kinetic model. However, the computational cost of the post-processing can be an issue, especially with sophisticated kinetic models. In this work, we propose a rapid method to perform perfusion estimation by replacing kinetic models with pre-trained neural networks. Two neural networks were trained to replace the kinetic model with or without gamma dispersion effects. The dispersion neural network is shown to achieve a lower computational cost.

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