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Anatomy of the Heart

Anatomy of the Heart

The human heart is made up of three layers of tissue that are surrounded by the pericardium, a membrane that protects and confines the heart. The outermost layer, closest to the pericardium, is the epicardium. The pericardial cavity separates the pericardium from the epicardium. Beneath the epicardium is the myocardium, the middle layer, and the endocardium, the innermost layer. There are four chambers of the heart: the right atrium, the right ventricle, the left atrium, and the left ventricle.

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Related Experiment Video

Updated: Jul 23, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning.

Daniel H Pak, Minliang Liu, Theodore Kim

IEEE Transactions on Medical Imaging

|July 11, 2023

Summary

This summary is machine-generated.

DeepCarve, a deep learning method, automatically creates patient-specific cardiac volumetric meshes quickly and accurately. This accelerates biomechanical studies, including stress analysis and stent simulations, without manual post-processing.

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Last Updated: Jul 23, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

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Published on: May 27, 2022

Area of Science:

Biomedical Engineering
Computational Mechanics
Medical Imaging

Background:

Automated volumetric meshing of patient-specific heart geometry is crucial for biomechanics studies.
Existing methods often fail to adequately model thin structures like valve leaflets.
Accurate meshing is essential for reliable downstream analyses, such as stress estimation.

Purpose of the Study:

To introduce DeepCarve, a novel deep learning method for automated patient-specific cardiac volumetric mesh generation.
To achieve high spatial accuracy and element quality in generated meshes.
To enable rapid and direct use of meshes for finite element analysis.

Main Methods:

A deformation-based deep learning approach is utilized.
Minimally sufficient surface mesh labels ensure precise spatial accuracy.
Simultaneous optimization of isotropic and anisotropic deformation energies enhances volumetric mesh quality.
Deep Cardiac Volumetric Mesh (DeepCarve) framework.

Main Results:

DeepCarve generates patient-specific volumetric meshes with high spatial accuracy and element quality.
Mesh generation achieves an inference time of 0.13 seconds per scan.
Generated meshes are directly usable for finite element analyses without manual intervention.
Incorporation of calcification meshes is supported for enhanced simulation accuracy.
Validation through numerous stent deployment simulations demonstrates viability for large-batch analyses.

Conclusions:

DeepCarve offers a significant advancement in automated cardiac volumetric meshing.
The method accelerates biomechanical simulations by providing high-quality, ready-to-use meshes.
DeepCarve has the potential to expedite cardiac research and clinical applications.