Motion information from 4D medical imaging offers critical insights into dynamic changes in patient anatomy for clinical assessments and radiotherapy planning and, thereby, enhances the capabilities of 3D image analysis. However, inherent physical and technical constraints of imaging hardware often necessitate a compromise between temporal resolution and image quality. Frame interpolation emerges as a pivotal solution to this challenge. Previous methods often suffer from discretion when they estimate the intermediate motion and execute the forward warping. In this study, we draw inspiration from fluid mechanics to propose a novel approach for continuously modeling patient anatomic motion using implicit neural representation. It ensures both spatial and temporal continuity, effectively bridging Eulerian and Lagrangian specifications together to naturally facilitate continuous frame interpolation. Our experiments across multiple datasets underscore the method's superior accuracy and speed. Furthermore, as a case-specific optimization (training-free) approach, it circumvents the need for extensive datasets and addresses model generalization issues.

翻译：四维医学成像中的运动信息为临床评估和放疗计划提供了患者解剖结构动态变化的关键洞察，从而增强了三维图像分析的能力。然而，成像硬件固有的物理和技术限制常常迫使在时间分辨率与图像质量之间做出妥协。帧插值技术成为应对这一挑战的关键解决方案。先前的方法在估计中间运动和执行前向变形时常常存在离散化问题。在本研究中，我们受流体力学启发，提出了一种利用隐式神经表示对患者解剖运动进行连续建模的新方法。该方法确保了空间和时间上的连续性，有效地将欧拉描述与拉格朗日描述相结合，从而自然地支持连续帧插值。我们在多个数据集上的实验证明了该方法在精度和速度上的优越性。此外，作为一种针对具体案例的优化（无需训练）方法，它避免了对大规模数据集的需求，并解决了模型泛化问题。