  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
