Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Disparate privacy risks from medical AI.

Nature·2026

Same author

Local and global patterns support medical imaging as a biomarker of ageing.

Communications medicine·2026

Same author

Longitudinal Language-Model Reasoning Enables Automated Labeling of Lung Cancer Recurrence from Unstructured Clinical Records.

Research square·2026

Same author

Multiparametric Free-Breathing 3D Whole-Heart Cardiac MR for Anatomical Bright- and Black-Blood Imaging With Co-Registered <math><semantics><mrow><msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn></mrow></msub> <mo>/</mo> <msub><mrow><mi>T</mi></mrow> <mrow><mn>2</mn></mrow></msub></mrow> <annotation>$$ {T}_1/{T}_2 $$</annotation></semantics></math> Myocardial Tissue Mapping at <math><semantics><mrow><mn>0</mn> <mo>.</mo> <mn>55</mn></mrow> <annotation>$$ 0.55 $$</annotation></semantics></math> T.

NMR in biomedicine·2026

Same author

A Maturity Model for the Enforcement of PETs in Federated Settings.

Studies in health technology and informatics·2026

Same author

A deep-learning framework reveals whole-body perturbations at cell level.

Nature·2026

Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026

Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026

Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026

Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026

Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026

Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 9, 2025

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning.

Qingjie Meng, Wenjia Bai, Declan P O'Regan

IEEE Transactions on Medical Imaging

|December 8, 2023

Summary

This summary is machine-generated.

DeepMesh estimates 3D cardiac motion using a novel mesh-based framework from cardiac magnetic resonance (CMR) images. This method accurately tracks heart movement, improving cardiac function assessment and disease diagnosis.

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Published on: October 2, 2021

Related Experiment Videos

Last Updated: Jul 9, 2025

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

Published on: October 2, 2021

Area of Science:

Medical Imaging
Biomedical Engineering
Computational Anatomy

Background:

Accurate 3D motion estimation from cardiac magnetic resonance (CMR) images is crucial for diagnosing cardiovascular diseases and assessing cardiac function.
Current methods often estimate dense motion fields in image space, neglecting the anatomical relevance within the heart.
There is a need for methods that focus motion estimation within the specific anatomical structures of interest.

Purpose of the Study:

To introduce DeepMesh, a novel learning framework for 3D cardiac motion estimation from CMR images.
To model the heart as a 3D mesh and estimate its motion for individual subjects.
To leverage 2D CMR data from multiple views for robust 3D mesh reconstruction and motion tracking.

Main Methods:

Modeling the heart using 3D meshes of the epicardial and endocardial surfaces.
Propagating a template heart mesh to subject-specific spaces and reconstructing the end-diastolic frame mesh.
Estimating mesh-based 3D motion fields from 2D short- and long-axis CMR images using a differentiable mesh-to-image rasterizer.
Maintaining vertex correspondences across time frames for quantitative functional assessment.

Main Results:

DeepMesh successfully reconstructs subject-specific heart meshes and estimates 3D motion from CMR images.
The method leverages multi-view 2D shape information for accurate 3D mesh reconstruction and motion estimation.
Quantitative and qualitative evaluations on UK Biobank CMR data demonstrate superior performance compared to existing methods.
Focus on 3D motion estimation of the left ventricle showed significant improvements.

Conclusions:

DeepMesh offers a robust and accurate approach for 3D cardiac motion estimation using CMR imaging.
The mesh-based framework provides consistent vertex correspondences, enabling precise cardiac function analysis.
This novel method outperforms traditional image-based and other mesh-based techniques in cardiac motion tracking.