Vicente Grau

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University of Oxford

United Kingdom

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Recent Grants

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Defining Personalised Management Strategies in Patients with Abdominal Aortic Aneurysms

Open Date: 2021-10-17

Close Date: 2025-10-16

Grant: Close

Generation of echocardiogram images for 3D image enhancement and localisation

Open Date: 2020-09-30

Close Date: 2024-09-29

Grant: Close

Understanding the impact of the microvasculature on quantification of fibre orientation in the heart using Diffusion Spectrum MRI and computer models

Open Date: 2012-10-30

Close Date: 2016-04-28

Grant: Close

3D histologically-detailed reconstruction of individual beating hearts: tools and application

Open Date: 2011-11-01

Close Date: 2015-05-30

Grant: Close

Mechanisms of ventricular wall deformation revealed by quantitative, imaging-based computer models incorporating sheet- and fibre-dynamics of normal and diseased heart

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Articles (18)

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2022

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2021

Collaborators (3)

Regent Lee

University of Oxford

UNITED KINGDOM

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Torben Lange

University Medical Center Göttingen

GERMANY

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Chen Chen

University of Sheffield

UNITED KINGDOM

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Vicente Grau

Research Interests

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Contact this professor

Recent Grants

Articles (18)

Large Language Model-Informed ECG Dual Attention Network for Heart Failure Risk Prediction

Deep learning based coronary vessels segmentation in X-ray angiography using temporal information

Personalized topology-informed localization of standard 12-lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins

Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey

NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation

Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference

Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks

Multi-modality cardiac image computing: A survey

Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.

A Deep Learning Approach to Visualize Aortic Aneurysm Morphology Without the Use of Intravenous Contrast Agents.

Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images

Interpretable cardiac anatomy modeling using variational mesh autoencoders.

Automated 3D Whole-Heart Mesh Reconstruction From 2D Cine MR Slices Using Statistical Shape Model.

Automated Torso Contour Extraction from Clinical Cardiac MR Slices for 3D Torso Reconstruction.

Multi-Domain Variational Autoencoders for Combined Modeling of MRI-Based Biventricular Anatomy and ECG-Based Cardiac Electrophysiology.

A deep learning pipeline to simulate fluorodeoxyglucose (FDG) uptake in head and neck cancers using non-contrast CT images without the administration of radioactive tracer.

Understanding and Improving Risk Assessment After Myocardial Infarction Using Automated Left Ventricular Shape Analysis.

A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices.

Collaborators (3)

Regent Lee

Torben Lange

Chen Chen

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