Enabling Chemical Tomography of Large Objects

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Our company has developed advanced chemical imaging capabilities which we offer as a service to industry, helping our clients accelerate their R&D. Our imaging approaches yield rich and large datasets that contain an abundance of physico-chemical information. This project will use artificial intelligence approaches to reconstruct X-ray scatter-based chemical tomography data from large objects.Large objects pose a problem due to geometric blurring of the scattered signals on the receiving detector, preventing conventional reconstruction approaches. We have spent considerable resources developing a non-linear least-squares algorithm to address this but it is computationally demanding and because of this imposes resolution limits on the reconstructed data (i.e. small images size). We have realised though that the problem has several features which indicate that it can be tackled by using deep learning approaches. Additionally, we have the ability to generate very large simulated labelled datasets that can be used as training sets for supervised learning using convolutional neural networks (CNNs). This is in addition the very large real data sets we have at our disposal. Whilst there are existing attempts to reconstruct conventional tomography data using CNNs, we are planning to develop new CNNs for reconstructing chemical (hyperspectral) tomography data and indeed overcome the parallax problem. The project thus is innovative both in terms of approach and application and will push the opportunities in this emerging field.