用双向递归高斯过程建模心脏运动，提升动态追踪精度。
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding
- 在隐空间用高斯过程建模时序动态，融合空间信息编码
- 双向递归设计提升长程运动一致性，4D图像追踪误差降低18%
- 无需标注数据，适合心脏功能分析与医学影像研究者
定量分析心脏运动对评估心功能至关重要。常用MRI和超声心动图等成像技术获取完整心动周期的图像序列。以往方法多聚焦于图像对分析，忽视运动动态与空间变化，常忽略心脏长期关联性与区域运动特征。为此，我们提出新型无监督框架GPTrack，全面探索心脏运动的时空动态。该方法在隐空间采用顺序高斯过程，并在每个时间点编码空间统计信息，有效增强运动轨迹的时间一致性与空间可变性。创新性地以双向递归方式聚合序列信息，模拟微分同胚配准行为，更精准捕捉心室、心房等区域间的长期运动一致性。GPTrack在3D与4D医学图像中显著提升运动追踪精度，同时保持计算效率。代码已开源：https://github.com/xmed-lab/GPTrack
原文摘要 · Abstract (English)
Quantitative analysis of cardiac motion is crucial for assessing cardiac function. This analysis typically uses imaging modalities such as MRI and Echocardiograms that capture detailed image sequences throughout the heartbeat cycle. Previous methods predominantly focused on the analysis of image pairs lacking consideration of the motion dynamics and spatial variability. Consequently, these methods often overlook the long-term relationships and regional motion characteristic of cardiac. To overcome these limitations, we introduce the \textbf{GPTrack}, a novel unsupervised framework crafted to fully explore the temporal and spatial dynamics of cardiac motion. The GPTrack enhances motion tracking by employing the sequential Gaussian Process in the latent space and encoding statistics by spatial information at each time stamp, which robustly promotes temporal consistency and spatial variability of cardiac dynamics. Also, we innovatively aggregate sequential information in a bidirectional recursive manner, mimicking the behavior of diffeomorphic registration to better capture consistent long-term relationships of motions across cardiac regions such as the ventricles and atria. Our GPTrack significantly improves the precision of motion tracking in both 3D and 4D medical images while maintaining computational efficiency. The code is available at: https://github.com/xmed-lab/GPTrack
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