The ability to map left ventricle (LV) myocardial motion using computed tomography angiography (CTA) is essential to diagnosing cardiovascular conditions and guiding interventional procedures. Due to their inherent locality, conventional neural networks typically have difficulty predicting subtle tangential movements, which considerably lessens the level of precision at which myocardium three-dimensional (3D) mapping can be performed. Using 3D optical flow techniques and Functional Maps (FMs), we present a comprehensive approach to address this problem. FMs are known for their capacity to capture global geometric features, thus providing a fuller understanding of 3D geometry. As an alternative to traditional segmentation-based priors, we employ surface-based two-dimensional (2D) constraints derived from spectral correspondence methods. Our 3D deep learning architecture, based on the ARFlow model, is optimized to handle complex 3D motion analysis tasks. By incorporating FMs, we can capture the subtle tangential movements of the myocardium surface precisely, hence significantly improving the accuracy of 3D mapping of the myocardium. The experimental results confirm the effectiveness of this method in enhancing myocardium motion analysis. This approach can contribute to improving cardiovascular diagnosis and treatment. Our code and additional resources are available at: https://shaharzuler.github.io/CardioSpectrumPage

翻译：利用计算机断层扫描血管造影（CTA）绘制左心室（LV）心肌运动的能力对于诊断心血管疾病和指导介入手术至关重要。由于传统神经网络固有的局部性，它们通常难以预测细微的切向运动，这大大降低了心肌三维（3D）映射所能达到的精度水平。我们利用3D光流技术和功能映射（FMs），提出了一种解决此问题的综合方法。FMs以其捕捉全局几何特征的能力而闻名，从而能更全面地理解3D几何结构。作为传统基于分割的先验信息的替代方案，我们采用了源自谱对应方法的基于表面的二维（2D）约束。我们基于ARFlow模型的三维深度学习架构经过优化，以处理复杂的三维运动分析任务。通过融入FMs，我们能够精确捕捉心肌表面的细微切向运动，从而显著提高心肌三维映射的准确性。实验结果证实了该方法在增强心肌运动分析方面的有效性。该方法有助于改善心血管疾病的诊断与治疗。我们的代码及其他资源可在以下网址获取：https://shaharzuler.github.io/CardioSpectrumPage