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 Videos

Patient specific physical anatomy models.

B M Cameron1, D R Holmes, M E Rettmann

  • 1Mayo Clinic College of Medicine, Rochester, MN 55905, USA.

Studies in Health Technology and Informatics
|April 9, 2008
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

An event-driven distributed processing architecture for image-guided cardiac ablation therapy.

Computer methods and programs in biomedicine·2009
Same author

An integrated system for real-time image guided cardiac catheter ablation.

Studies in health technology and informatics·2006
Same author

Patient specific dynamic geometric models from sequential volumetric time series image data.

Studies in health technology and informatics·2004
Same author

An axial skeleton based surface deformation algorithm for patient specific anatomic modeling.

Studies in health technology and informatics·2000
Same author

A prospective study of serum pseudocholinesterase levels in patients with chronic spinal pain: a preliminary study.

Spine·2000
Same author

Timing of coronary stent thrombosis in patients treated with ticlopidine and aspirin.

The American journal of cardiology·1999
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

Patient-specific anatomical models created using 3D printing enable realistic validation of image-guided interventions. This approach reduces the need for in-vivo experiments, saving time and resources in medical device development.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • 3D Printing Technology

Background:

  • Patient-specific anatomical models are crucial for validating medical interventions.
  • Traditional validation methods can be time-consuming and resource-intensive.

Purpose of the Study:

  • To describe a general approach for creating patient-specific physical models for validating image-guided interventions.
  • To introduce a patient emulation system for realistic validation scenarios.

Main Methods:

  • Utilizing small footprint stereo-lithographic printers.
  • Employing readily available segmentation and surface modeling software.
  • Developing both non-tissue and tissue-mimicking models.

Main Results:

Related Experiment Videos

  • Demonstrated the feasibility of creating patient-specific anatomical models.
  • Established a system for validating image-guided interventions under realistic conditions.
  • Reduced the necessity for extensive in-vivo validation experiments.

Conclusions:

  • Patient-specific models offer a powerful tool for validating image-guided interventions.
  • The described approach streamlines the validation process, enhancing efficiency.
  • This methodology supports the development and refinement of clinical applications.