Correcting bias in cardiac geometries derived from multimodal images using spatiotemporal mapping.

ABSTRACT: Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function be

SUBMITTER: Zhao D 

PROVIDER: S-EPMC10199025 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature