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 Experiment Video

Updated: May 15, 2026

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
08:52

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

Published on: November 27, 2017

An efficient algorithm for mapping imaging data to 3D unstructured grids in computational biomechanics.

Daniel R Einstein1, Andrew P Kuprat, Xiangmin Jiao

  • 1Systems Toxicology, Pacific Northwest National Laboratory, Richland, WA, U.S.A. daniel.einstein@pnnl.gov

International Journal for Numerical Methods in Biomedical Engineering
|January 8, 2013
PubMed
Summary
This summary is machine-generated.

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

The Common Fund Data Ecosystem (CFDE).

bioRxiv : the preprint server for biology·2026
Same author

Accelerating correlated wave function calculations with hierarchical matrix compression of the two-electron integrals.

The Journal of chemical physics·2025
Same author

Sorted-cell proteomics reveals an AT1-associated epithelial cornification phenotype and suggests endothelial redox imbalance in human bronchopulmonary dysplasia.

American journal of physiology. Lung cellular and molecular physiology·2025
Same author

Sorted-Cell Proteomics Reveals an AT1-Associated Epithelial Cornification Phenotype and Suggests Endothelial Redox Imbalance in Human Bronchopulmonary Dysplasia.

bioRxiv : the preprint server for biology·2025
Same author

Efficient and scalable wave function compression using corner hierarchical matrices.

The Journal of chemical physics·2024
Same author

Cell Population-resolved Multiomics Atlas of the Developing Lung.

American journal of respiratory cell and molecular biology·2024
Same journal

Functional and Structural Analysis of Auxetic Metamaterial Stent Designs for Thoracic Aortic Aneurysms Using Finite Element Simulation.

International journal for numerical methods in biomedical engineering·2026
Same journal

Dynamic Simulation of Male Bladder Outlet Obstruction: Flow Characteristics and Novel Quantitative Indicators.

International journal for numerical methods in biomedical engineering·2026
Same journal

Path Planning of Flexible Needle Based on Improved Particle Swarm Optimization Algorithm.

International journal for numerical methods in biomedical engineering·2026
Same journal

A Fully Automated Pipeline for Vertebral Structural Assessment From Medical Images. Application Under Metastatic Conditions.

International journal for numerical methods in biomedical engineering·2026
Same journal

Evaluation of Degradable Mg-Alloy Implants for Femoral Neck Fractures: Subject-Specific Finite Element Analysis Integrated With Bone Remodelling.

International journal for numerical methods in biomedical engineering·2026
Same journal

Patient-Specific In Silico Prediction of Outcomes of Partial Continuous-Flow LVAD Treatment in Peripartum Cardiomyopathy.

International journal for numerical methods in biomedical engineering·2026
See all related articles

This study introduces a new algorithm for efficiently mapping complex medical imaging data, such as diffusion tensor imaging and histology, onto computational grids for organ simulations. This enhances the accuracy of multiscale organ function modeling.

Area of Science:

  • Computational biology
  • Medical imaging analysis
  • Multiscale modeling

Background:

  • Organ function simulations increasingly rely on imaging data for geometry.
  • Medical images contain valuable spatial data beyond geometry, crucial for simulation parameters and initial conditions.
  • Existing methods for mapping this data to simulation grids can be inefficient.

Purpose of the Study:

  • To develop an efficient and robust algorithm for mapping spatially heterogeneous imaging data to unstructured polyhedral grids.
  • To demonstrate the algorithm's versatility across different biological datasets and simulation scales.
  • To assess the computational performance and parallel scalability of the mapping technique.

Main Methods:

  • Developed a novel parallel algorithm for data mapping to unstructured polyhedral grids.

More Related Videos

An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production
07:46

An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production

Published on: March 27, 2017

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

Related Experiment Videos

Last Updated: May 15, 2026

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
08:52

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

Published on: November 27, 2017

An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production
07:46

An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production

Published on: March 27, 2017

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
12:49

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Published on: September 28, 2019

  • Applied the algorithm to map MRI diffusion tensor data to ventricular grids.
  • Mapped serial cryosection histology data to mouse brain grids.
  • Mapped CT-derived volumetric strain data to multiscale lung grids.

    • Main Results:

    • Successfully mapped diverse imaging data (MRI, histology, CT) to corresponding anatomical grids.
    • Demonstrated efficient and robust performance of the mapping algorithm across all test cases.
    • Reported execution times and parallel performance metrics, validating the algorithm's scalability.

    Conclusions:

    • The presented algorithm provides an efficient and robust solution for integrating complex imaging data into organ simulation frameworks.
    • This method facilitates more accurate and comprehensive multiscale simulations of organ function.
    • The parallel implementation ensures scalability for large-scale biomedical imaging and simulation applications.