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.

翻译：计算机断层成像（CT）凭借其从多角度投影实现三维成像的卓越能力，在临床诊断、科学观测和工业检测等领域具有广泛应用。神经自适应断层成像（NeAT）是近期提出的基于神经辐射场的CT三维重建方法，相较于传统方法展现出更优性能。然而，该方法在处理CT扫描过程中显著存在的扰动与位姿偏移问题时仍面临挑战。本文提出一种用于CT重建的神经渲染方法——迭代式神经自适应断层成像（INeAT），该方法通过引入迭代位姿优化机制，有效抑制数据中的位姿扰动影响，尤其适用于大幅位姿变化场景。基于位姿反馈优化策略，INeAT根据重建后的三维体数据迭代优化输入图像对应的位姿参数。研究表明，INeAT在大幅位姿扰动场景下可实现伪影抑制与分辨率增强的重建效果。此外，即便使用非稳定状态采集数据，INeAT仍能保持与稳定状态采集相当的重建性能，这显著缩短了CT扫描所需时间并降低了对成像硬件系统的严苛要求，充分展现了其在短时、低成本CT技术中的巨大应用潜力。