Computed Tomography (CT) with its remarkable capability for three-dimensional imaging from multiple projections, enjoys a broad range of applications in clinical diagnosis, scientific observation, and industrial detection. Neural Adaptive Tomography (NeAT) is a recently proposed 3D rendering method based on neural radiance field for CT, and it demonstrates superior performance compared to traditional methods. However, it still faces challenges when dealing with the substantial perturbations and pose shifts encountered in CT scanning processes. Here, we propose a neural rendering method for CT reconstruction, named Iterative Neural Adaptive Tomography (INeAT), which incorporates iterative posture optimization to effectively counteract the influence of posture perturbations in data, particularly in cases involving significant posture variations. Through the implementation of a posture feedback optimization strategy, INeAT iteratively refines the posture corresponding to the input images based on the reconstructed 3D volume. We demonstrate that INeAT achieves artifact-suppressed and resolution-enhanced reconstruction in scenarios with significant pose disturbances. Furthermore, we show that our INeAT maintains comparable reconstruction performance to stable-state acquisitions even using data from unstable-state acquisitions, which significantly reduces the time required for CT scanning and relaxes the stringent requirements on imaging hardware systems, underscoring its immense potential for applications in short-time and low-cost CT technology.

提出了一种称为迭代神经自适应层析的计算机断层扫描重建方法，利用姿势优化策略迭代地优化输入图像对应的姿势，从而在具有明显姿势变化的情况下实现了抑制伪影和提高分辨率的重建，同时减少了CT扫描所需的时间，并放松了对成像硬件系统的严格要求，具有很大的短时间和低成本CT技术应用潜力。

INeAT: 迭代神经自适应层析成像