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Abstract #4094

Using Machine Learning to accelerate fat-based head motion-navigators – a preliminary simulation study

Daniel Gallichan1

1CUBRIC, Cardiff University, Cardiff, United Kingdom

We hypothesise that machine learning approaches could be applied to speed up motion-correction navigators – potentially obviating the need for image reconstruction and co-registration. In this preliminary 2D simulation study we investigate the ability of a simple 5-layer convolutional neural network to predict motion-parameters based on difference images between two head poses. Our results indicate that the CNN is able to outperform linear regression over the range of parameters tested, supporting our aim to develop this concept in more detailed future work.

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