US 2026/0087623 A1 · FUNCTIONAL STENOSIS ASSESSMENT FROM VASCULAR IMAGING

Abstract

The present disclosure provides to generate ground truth data for training a machine learning (ML) model to infer pressure information (e.g., a pressure curve, a pressure ratio, or the like) for a cardiac artery from border segmentations generated from images of the cardiac artery. The ground truth data can comprise vessel and/or lumen segmentations for several cardiac arteries and associated pressure information for the cardiac arteries. The vessel and/or lumen segmentations can be generated from images from different image modalities (e.g., IVUS, angiographic, CT, etc.). Further, some of the associated pressure information can be based on measured pressure information (e.g., using a pressure sensing catheter) while other associated pressure information can be derived from the vessel and/or lumen border segmentations using numerical analysis techniques (e.g., CFD, or the like).

Claims20

Dependent claims

A computing system to train a machine learning (ML) model to infer pressure information for a cardiac artery from border segmentations of the cardiac artery, comprising:

a processor;
and
memory comprising instructions executable by the processor, which instructions when executed cause the computing system to:
receive vessel and/or lumen border segmentations for a plurality of cardiac arteries;
generate models of the plurality of cardiac arteries from the vessel and/or lumen border segmentations;
derive pressure information for each of the plurality of cardiac arteries based in part on solving a system of equations defining the pressure information using numerical analysis applied to the models of the plurality of cardiac arteries;
and
add the vessel and/or lumen border segmentations and the associated derived pressure information to ground truth data for training a machine learning (ML) model.

5 more elements

A method for forming ground truth data to train a machine learning (ML) model to infer pressure information for a cardiac artery from border segmentations of the cardiac artery, comprising:

receiving vessel and/or lumen border segmentations for a plurality of cardiac arteries;
generating models of the plurality of cardiac arteries from the vessel and/or lumen border segmentations;
deriving pressure information for each of the plurality of cardiac arteries based in part on solving a system of equations defining the pressure information using numerical analysis applied to the models of the plurality of cardiac arteries;
and
adding the vessel and/or lumen border segmentations and the associated derived pressure information to ground truth data for training a machine learning (ML) model.

2 more elements

Dependencies are inferred from the claim text.

Functional stenosis assessment from vascular imaging

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Functional stenosis assessment from vascular imaging

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Abstract

Claims20