University of Michigan

King's College London

Brahmajee Kartik Nallamothu

Raj Rao Nadakuditi

Carlos Alberto Figueroa-Alvarez

Kritika Iyer

Krishnakumar Garikipati

Christopher John Arthurs

Elizabeth Renee Livingston

Kevin R Ward

Kayvan Najarian

Sayedmohammadreza Soroushmehr

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

A system analyzes angiogram image data, including video data, in order to extract vasculature information. Advanced image processing and machine learning techniques are used in pre-processing, frame selection, and vasculature segmentation to remove classes of artifacts from angiogram videos, and specifically from frames selected as having a sufficient amount of image data. From segmentation, accurate vasculature diameters are calculated, and, in some examples, stenoses and/or the extent of stenosis is automatically determined and displayed.

Automated analysis of vasculature in coronary angiograms

Growing community of inventors

Ann Arbor, MI, United States of America

Brahmajee Kartik Nallamothu