Perfusion parameter estimation using neural networks and data augmentation

David Robben; Paul Suetens

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arXiv · 1810.04898

Perfusion parameter estimation using neural networks and data augmentation

David Robben·Paul Suetens

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Abstract

Perfusion imaging plays a crucial role in acute stroke diagnosis and treatment decision making. Current perfusion analysis relies on deconvolution of the measured signals, an operation that is mathematically ill-conditioned and requires strong regularization. We propose a neural network and a data augmentation approach to predict perfusion parameters directly from the native measurements. A comparison on simulated CT Perfusion data shows that the neural network provides better estimations for both CBF and Tmax than a state of the art deconvolution method, and this over a wide range of noise levels. The proposed data augmentation enables to achieve these results with less than 100 datasets.

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BibTeX RIS

David Robben, Paul Suetens. 2018-10-11. Perfusion parameter estimation using neural networks and data augmentation. https://arxiv.org/abs/1810.04898

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