用隐式神经表示提升心脏影像动态配准精度
Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle
- 用隐式神经网络在连续点上建模心肌运动
- 结合距离场与CT值，配准误差显著降低
- 适合心脏功能分析的高精度时序建模
理解左心室心肌（LVmyo）在心动周期中的运动对评估心脏功能至关重要。通过一系列可变形图像配准（DIR）来建模这种运动是一种有效方法。传统基于卷积神经网络的深度学习DIR方法通常需要大量内存和计算资源。相比之下，隐式神经表示（INRs）通过在任意数量的连续点上操作，提供了更高效的解决方案。本研究将INRs扩展应用于心脏计算机断层扫描（CT），聚焦于LVmyo的配准。为提高心肌边缘区域的配准精度，我们融合了心肌的符号距离场与CT帧的亨氏单位（Hounsfield Unit）值，引导心肌配准的同时保留组织信息。该框架展现出高精度的配准效果，提供了一种稳健的时序配准方法，有助于进一步分析心肌运动。
原文摘要 · Abstract (English)
Understanding the movement of the left ventricle myocardium (LVmyo) during the cardiac cycle is essential for assessing cardiac function. One way to model this movement is through a series of deformable image registrations (DIRs) of the LVmyo. Traditional deep learning methods for DIRs, such as those based on convolutional neural networks, often require substantial memory and computational resources. In contrast, implicit neural representations (INRs) offer an efficient approach by operating on any number of continuous points. This study extends the use of INRs for DIR to cardiac computed tomography (CT), focusing on LVmyo registration. To enhance the precision of the registration around the LVmyo, we incorporate the signed distance field of the LVmyo with the Hounsfield Unit values from the CT frames. This guides the registration of the LVmyo, while keeping the tissue information from the CT frames. Our framework demonstrates high registration accuracy and provides a robust method for temporal registration that facilitates further analysis of LVmyo motion.
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