Enabling Chemical Tomography of Large Objects

我们的公司开发了先进的化学成像能力，我们为行业提供服务，帮助我们的客户加快他们的研发。我们的成像方法产生了丰富的大型数据集，其中包含大量的物理化学信息。该项目将使用人工智能方法从大对象中重建基于X射线散射的化学层析数据。大对象由于接收探测器上的散射信号的几何模糊而造成问题，阻碍了传统的重建方法。我们已经花费了相当多的资源来开发一种非线性最小二乘算法来解决这个问题，但它对计算要求很高，因此对重建数据(即小图像尺寸)施加了分辨率限制。然而，我们已经意识到，这个问题有几个特点，表明它可以通过使用深度学习方法来解决。此外，我们还能够生成非常大的模拟标记数据集，这些数据集可以用作使用卷积神经网络(CNN)进行监督学习的训练集。这是我们拥有的非常大的真实数据集之外的东西。虽然已有使用CNN重建常规断层扫描数据的现有尝试，但我们正计划开发用于重建化学(高光谱)断层扫描数据的新CNN，并且确实克服了视差问题。因此，该项目在方法和应用方面都是创新的，并将推动这一新兴领域的机会。

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.