This paper presents a novel method for the reconstruction of high-resolution temporal images in dynamic tomographic imaging, particularly for discrete objects with smooth boundaries that vary over time. Addressing the challenge of limited measurements per time point, we propose a technique that synergistically incorporates spatial and temporal information of the dynamic objects. This is achieved through the application of the level-set method for image segmentation and the representation of motion via a sinusoidal basis. The result is a computationally efficient and easily optimizable variational framework that enables the reconstruction of high-quality 2D or 3D image sequences with a single projection per frame. Compared to current methods, our proposed approach demonstrates superior performance on both synthetic and pseudo-dynamic real X-ray tomography datasets. The implications of this research extend to improved visualization and analysis of dynamic processes in tomographic imaging, finding potential applications in diverse scientific and industrial domains.

翻译：本文提出了一种用于动态断层成像中高分辨率时间图像重建的新方法，特别针对具有随时间变化的平滑边界的离散对象。针对每个时间点的有限测量值这一挑战，我们提出了一种协同整合动态对象空间与时间信息的技术。该技术通过应用水平集方法进行图像分割，并利用正弦基函数表示运动来实现。最终构建了一个计算高效且易于优化的变分框架，使得每帧仅需单次投影即可重建高质量的二维或三维图像序列。与现有方法相比，本方法在合成数据集和伪动态真实X射线断层成像数据集上均展现出更优性能。该研究为改进断层成像中动态过程的可视化与分析提供了新途径，在科学与工业领域具有广泛的应用潜力。