2023
DOI: 10.1038/s41598-023-33968-5
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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 geom… Show more

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Cited by 4 publications
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