University of Michigan

King's College London

Carlos Alberto Figueroa-Alvarez

Brahmajee Kartik Nallamothu

Raj Rao Nadakuditi

Kritika Iyer

Krishnakumar Garikipati

Christopher John Arthurs

Elizabeth Renee Livingston

Ismael Assi

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.

Machine learning approach for coronary 3D reconstruction from X-ray angiography images

Anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques deploying methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.

Anatomical and functional assessment of CAD using machine learning

Anatomical and functional assessment of coronary artery disease using machine learning

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Ann Arbor, MI, United States of America

Carlos Alberto Figueroa-Alvarez