标题： 心脏周期中左心室心肌配准的隐式神经表示 显示英文标题

标题： Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle

摘要： 理解左心室心肌（LVmyo）在心脏周期中的运动对于评估心脏功能至关重要。 一种建模这种运动的方法是通过一系列可变形图像配准（DIRs）的LVmyo。 传统的基于卷积神经网络的DIR深度学习方法通常需要大量的内存和计算资源。 相反，隐式神经表示（INRs）通过在任意数量的连续点上操作，提供了一种高效的解决方案。 本研究将INRs用于DIR扩展到心脏计算机断层扫描（CT），重点在于LVmyo配准。 为了提高LVmyo周围的配准精度，我们将LVmyo的有符号距离场与CT帧的Hounsfield单位值相结合。 这引导了LVmyo的配准，同时保留了CT帧中的组织信息。 我们的框架展示了高精度的配准效果，并提供了一种稳健的时间配准方法，有助于进一步分析LVmyo的运动。 显示英文摘要

摘要： 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.