Image2Flow: A hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data

no code implementations • 28 Feb 2024 • Tina Yao, Endrit Pajaziti, Michael Quail, Silvia Schievano, Jennifer A Steeden, Vivek Muthurangu

This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields.

Anatomy Segmentation

Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry

no code implementations • 21 Mar 2023 • Tina Yao, Nicole St. Clair, Gabriel F. Miller, Adam L. Dorfman, Mark A. Fogel, Sunil Ghelani, Rajesh Krishnamurthy, Christopher Z. Lam, Joshua D. Robinson, David Schidlow, Timothy C. Slesnick, Justin Weigand, Michael Quail, Rahul Rathod, Jennifer A. Steeden, Vivek Muthurangu

Purpose: To develop and evaluate an end-to-end deep learning pipeline for segmentation and analysis of cardiac magnetic resonance images to provide core-lab processing for a multi-centre registry of Fontan patients.

Segmentation

Rapid Whole-Heart CMR with Single Volume Super-resolution

no code implementations • 22 Dec 2019 • Jennifer A. Steeden, Michael Quail, Alexander Gotschy, Andreas Hauptmann, Simon Arridge, Rodney Jones, Vivek Muthurangu

Conclusion: This paper demonstrates the potential of using a residual U-Net for super-resolution reconstruction of rapidly acquired low-resolution whole heart bSSFP data within a clinical setting.

Anatomy Super-Resolution