A major challenge in computed tomography is reconstructing objects from incomplete data. An increasingly popular solution for these problems is to incorporate deep learning models into reconstruction algorithms. This study introduces a novel approach by integrating a Fourier neural operator (FNO) into the Filtered Backprojection (FBP) reconstruction method, yielding the FNO back projection (FNO-BP) network. We employ moment conditions for sinogram extrapolation to assist the model in mitigating artefacts from limited data. Notably, our deep learning architecture maintains a runtime comparable to classical filtered back projection (FBP) reconstructions, ensuring swift performance during both inference and training. We assess our reconstruction method in the context of the Helsinki Tomography Challenge 2022 and also compare it against regular FBP methods.

翻译：计算机断层成像领域的一个主要挑战是从不完整数据中重建物体。针对这类问题，将深度学习模型融入重建算法成为日益流行的解决方案。本研究提出一种新方法，将傅里叶神经算子（FNO）集成到滤波反投影（FBP）重建方法中，从而构建出FNO反投影（FNO-BP）网络。我们采用矩条件进行正弦图外推，以辅助模型减少有限数据带来的伪影。值得注意的是，我们的深度学习架构保持了与经典滤波反投影（FBP）重建相当的计算时间，在推理和训练过程中均能确保快速性能。我们以2022年赫尔辛基层析成像挑战赛为背景评估了重建方法，并将其与常规FBP方法进行了比较。