US 2026/0065475 A1 · Machine Learning Approach for Coronary 3D Reconstruction from X-ray Angiography Images

Abstract

A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.

Claims20

Dependent claims

A computer-implemented method for training a vessel tree reconstruction system, the method comprising:

selecting a set of three or more binary angiography images corresponding to three or more different projection angles for each synthetic vessel tree of a set of synthetic vessel trees;
applying a Euclidean distance transform encoding a two-dimensional (2D) diameter of each branch to the binary angiography images and generating Euclidean distance transformed angiography images;
and
training, based on the set of Euclidean distance transformed angiography images, a 3D vessel tree reconstruction machine learning model capable of reconstructing 3D vessels, wherein the training of the 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage.

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A method of performing 3D vessel tree reconstruction comprising:

providing segmented binary angiography images to a preprocessing module;
applying, by the preprocessing module, a Euclidean distance transform encoding a two-dimensional (2D) diameter of each branch to the binary angiography images and generating Euclidean distance transformed angiography images;
feeding the set of Euclidean distance transformed angiography images to a trained 3D vessel tree reconstruction machine learning model capable of reconstructing 3D vessels, wherein the trained 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage.

Dependencies are inferred from the claim text.

Machine Learning Approach for Coronary 3D Reconstruction from X-ray Angiography Images

Life of this patent

Machine Learning Approach for Coronary 3D Reconstruction from X-ray Angiography Images

Life of this patent

Abstract

Claims20