Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Posterior Electrical Axis Deviation Is Associated With Future Development of Left Bundle Branch Block: A UK Biobank Study.

JACC. Advances·2026
Same author

Filling the Gaps: Generating 4D Dense Cardiac Anatomy from Sparse CMR for Enhanced Tetralogy of Fallot Assessment.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2026
Same author

Modeling Aleatoric Uncertainty in Cardiac MRI Segmentation: Probabilistic Detection and Contour Regression.

IEEE transactions on medical imaging·2026
Same author

Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.

PLoS computational biology·2026
Same author

Multi-chamber three-dimensional myocardial strain assessment by computed tomography: a comparison with speckle tracking echocardiography and association with pulmonary hypertension in severe aortic stenosis.

Frontiers in cardiovascular medicine·2026
Same author

Computational analysis of intracardiac collision risk and optimal site for right ventricular leadless left bundle branch area pacing: A simulation study.

Heart rhythm O2·2026
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

Related Experiment Video

Updated: Apr 16, 2026

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse
15:26

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

Published on: May 19, 2015

15.0K

MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction.

Yu Deng, Yiyang Xu, Linglong Qian

    IEEE Transactions on Medical Imaging
    |April 14, 2026
    PubMed
    Summary
    This summary is machine-generated.

    MorphiNet, a novel network, reconstructs heart anatomy from Computed Tomography (CT) images for Cardiac Magnetic Resonance (CMR) analysis. It improves cardiac digital twin accuracy, outperforming existing methods in patient-specific geometry and function analysis.

    More Related Videos

    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
    10:30

    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

    Published on: October 15, 2014

    21.2K
    Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
    05:56

    Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

    Published on: August 9, 2024

    2.9K

    Related Experiment Videos

    Last Updated: Apr 16, 2026

    3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse
    15:26

    3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

    Published on: May 19, 2015

    15.0K
    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
    10:30

    Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

    Published on: October 15, 2014

    21.2K
    Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
    05:56

    Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

    Published on: August 9, 2024

    2.9K

    Area of Science:

    • Medical imaging analysis
    • Computational anatomy
    • Biomedical engineering

    Background:

    • Cardiac Magnetic Resonance (CMR) imaging is crucial for personalized cardiac digital twins but suffers from anisotropy and motion artifacts, limiting anatomical detail.
    • Existing methods struggle with data loss and measurement inaccuracies inherent in CMR data, hindering precise heart modeling.
    • Accurate patient-specific heart models are essential for understanding cardiac function and disease progression.

    Purpose of the Study:

    • To introduce MorphiNet, a novel network for reproducing patient-specific heart anatomy from high-resolution Computed Tomography (CT) images, without direct pairing with CMR data.
    • To enhance the accuracy and detail of cardiac anatomical structures derived from CMR imaging for improved digital twin analysis.
    • To develop a robust method for refining cardiac geometries and enabling accurate cardiac function analysis.

    Main Methods:

    • MorphiNet encodes anatomical structure as gradient fields, deforming template meshes into patient-specific geometries.
    • A multilayer graph subdivision network refines these geometries, maintaining dense point correspondence for computational analysis.
    • The network learns anatomy from unpaired high-resolution CT images, applicable to CMR data.

    Main Results:

    • MorphiNet achieved superior bi-ventricular myocardium reconstruction on CMR patients with tetralogy of Fallot, outperforming template-based methods (0.3 higher Dice, 2.6 lower Hausdorff).
    • It demonstrated comparable geometric accuracy to neural implicit functions on CT data, with 50x faster inference.
    • Cross-dataset validation showed robust generalization, achieving a 0.7 Dice score, a 30% improvement over previous template-based approaches.

    Conclusions:

    • MorphiNet effectively reconstructs cardiac anatomy from unpaired CT data, significantly improving patient-specific heart models for digital twin analysis.
    • The method enhances anatomical detail and accuracy in CMR-derived models, overcoming limitations of standard imaging techniques.
    • MorphiNet facilitates accurate cardiac function analysis, including ejection fraction estimation, and shows promise for identifying myocardial dysfunction.