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Conditional LDDM flow matching: an application to shape uncertainty quantification of biomedical models

Logo of Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS)Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS)
Logo of Leibniz-Institut für innovative Mikroelektronik (IHP)Leibniz-Institut für innovative Mikroelektronik (IHP)
 Katz, Sarah

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Conditional LDDM flow matching: an application to shape uncertainty quantification of biomedical models
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21
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CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Abstract
We present a new extension and application of prior work on LDDMM (Large Deformation Diffeomorphic Metric Mapping) flow matching to generative modeling for uncertainty quantification in the context of blood vessel geometries estimated from medical images. Conditional LDDMM flow matching is used to generate arrays of variants of existing aorta geometries; simulations of pulsatile flow on these are performed and the results compared in terms of medically relevant biomarkers.