Image monitoring and guidance during medical examinations can aid both diagnosis and treatment. However, the sampling frequency is often too low, which creates a need to estimate the missing images. We present a probabilistic motion model for sequential medical images, with the ability to both estimate motion between acquired images and forecast the motion ahead of time. The core is a low-dimensional temporal process based on a linear Gaussian state-space model with analytically tractable solutions for forecasting, simulation, and imputation of missing samples. The results, from two experiments on publicly available cardiac datasets, show reliable motion estimates and an improved forecasting performance using patient-specific adaptation by online learning.

翻译：在医学检查过程中进行图像监测与引导，既可辅助诊断，又能支持治疗。然而，图像采样频率通常过低，因此需要估计缺失的图像帧。本文提出一种用于序列医学图像的概率运动模型，该模型既能估计已采集图像间的运动，也能提前预测运动趋势。其核心是一个基于线性高斯状态空间模型的低维时序过程，该模型在预测、模拟及缺失样本插补方面具有解析可处理的解。通过对两个公开心脏数据集的实验，结果表明：通过在线学习实现的患者特异性自适应，能够提供可靠的运动估计并提升预测性能。