  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.
