Data collected from the X-ray fluorescence-computed tomography (XFCT) is frequently reconstructed with algorithms proposed for X-ray transmission tomography. As these algorithms do not model the self-absorption effect inherent to XFCT, their capacity on accurately reconstructing the elemental distribution is limited. Although algorithms specialized for XFCT reconstruction have been developed, the majority of them impose strict requirements on the samples and the acquisition setup. To relax these prerequisites, a deep convolutional neural network is proposed to correct the self-absorption effect in the sinogram domain. Through quantitative evaluation, we conclude that the well-trained neural network can correct fluorescence sinograms affected by the self-absorption effect. Furthermore, we demonstrate that such corrections enable conventional algorithms to reconstruct the elemental distribution with high fidelity. As the only input required by the proposed neural network is the fluorescence sinogram, it is fully automatic and is applicable to different scan setups and samples.

中文翻译：

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使用深度卷积神经网络的 X 射线荧光计算机断层扫描中的自吸收校正

从 X 射线荧光计算机断层扫描 (XFCT) 收集的数据经常使用为 X 射线透射断层扫描提出的算法进行重建。由于这些算法没有对 XFCT 固有的自吸收效应进行建模，因此它们准确重建元素分布的能力是有限的。尽管已经开发了专门用于 XFCT 重建的算法，但其中大多数对样本和采集设置提出了严格的要求。为了放宽这些先决条件，提出了一个深度卷积神经网络来纠正正弦图域中的自吸收效应。通过定量评估，我们得出结论，训练有素的神经网络可以校正受自吸收效应影响的荧光正弦图。此外，我们证明了这种校正使传统算法能够以高保真度重建元素分布。由于所提出的神经网络所需的唯一输入是荧光正弦图，它是全自动的，适用于不同的扫描设置和样本。