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

Electro-mechanical Systems01:19

Electro-mechanical Systems

Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
Galvanometer01:24

Galvanometer

Common devices, including car instrument panels, battery chargers, and inexpensive electrical instruments, measure potential difference (voltage), current, or resistance using a d'Arsonval galvanometer. This electromechanical instrument is also known as a moving coil galvanometer.
The galvanometer consists of  two concave-shaped permanent magnets, providing a uniform radial magnetic field in the annular region. In the center, a pivoted coil of fine copper wire is placed in the uniform magnetic...
Feedback control systems01:26

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Open and closed-loop control systems01:17

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
Gyroscope: Precession01:24

Gyroscope: Precession

Precession can be demonstrated effectively through a spinning top. If a spinning top is placed on a flat surface near the surface of the Earth at a vertical angle and is not spinning, it will fall over due to the force of gravity producing a torque acting on its center of mass. However, if the top is spinning on its axis, it precesses about the vertical direction, rather than topple over due to this torque. Precessional motion is a combination of a steady circular motion of the axis and the...

You might also read

Related Articles

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

Sort by
Same author

Erratum for: Associations of MRI-derived Paraspinal IMAT and LMM with Cardiometabolic Risk Factors: Results from a German Cohort.

Radiology·2026
Same author

ProtoFlow: interpretable and robust surgical workflow modeling with learned dynamic scene graph prototypes.

International journal of computer assisted radiology and surgery·2026
Same author

Toward comprehensive real-time scene understanding in ophthalmic surgery through multimodal image fusion.

International journal of computer assisted radiology and surgery·2026
Same author

DefSynUS: Real-time patient-specific intrahepatic vessel identification via deformation-aware CT-US domain adaptation.

International journal of computer assisted radiology and surgery·2026
Same author

Smartphone photogrammetry for rapid 3D surface modeling of head and neck specimens to support frozen section communication: A feasibility pilot study.

Oral oncology·2026
Same author

Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition·2026
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

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

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: May 9, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Electromagnetic servoing-a new tracking paradigm.

Tobias Reichl1, José Gardiazabal, Nassir Navab

  • 1Computer AidedMedical Procedures (CAMP), TechnischeUniversität München, 85748 Munich, Germany. reichl@tum.de

IEEE Transactions on Medical Imaging
|August 6, 2013
PubMed
Summary
This summary is machine-generated.

A new robotic system enhances electromagnetic (EM) tracking for computer-assisted interventions. By integrating EM tracking with robot arm positioning, it achieves uniform, high accuracy for flexible instruments within the body.

More Related Videos

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Method to Measure Tone of Axial and Proximal Muscle
10:41

Method to Measure Tone of Axial and Proximal Muscle

Published on: December 14, 2011

Related Experiment Videos

Last Updated: May 9, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Method to Measure Tone of Axial and Proximal Muscle
10:41

Method to Measure Tone of Axial and Proximal Muscle

Published on: December 14, 2011

Area of Science:

  • Medical technology
  • Robotics
  • Biomedical engineering

Background:

  • Electromagnetic (EM) tracking is crucial for computer-assisted interventions, especially for flexible instruments where line-of-sight tracking is impossible.
  • Current EM tracking systems lack uniform accuracy, with optimal precision limited to the central tracking volume.
  • A general solution for tracking flexible instruments within the human body remains an open challenge.

Purpose of the Study:

  • To introduce a novel EM tracking paradigm using a robot arm to achieve uniform and optimal accuracy.
  • To enhance the tracking volume and precision for minimally invasive procedures.
  • To address the limitations of conventional EM tracking systems in accuracy and coverage.

Main Methods:

  • Mounting an EM field generator onto a robot arm to create an EM servoing methodology.
  • Utilizing EM tracking to detect sensors within a known, high-accuracy sub-volume.
  • Employing robot positioning to maintain the sensor near the tracking volume's center for consistent accuracy.
  • Evaluating dynamic accuracy and accuracy distribution using optical tracking as ground truth.

Main Results:

  • The proposed EM servoing method significantly improved accuracy, reducing overall error from 6.64±7.86 mm to 3.83±6.43 mm.
  • The combined system expands the tracking volume, limited only by the robot's reach, not the EM generator's field.
  • Consistent and optimal accuracy was achieved throughout the expanded tracking volume.

Conclusions:

  • The robot-assisted EM tracking system offers a robust solution for precise instrument localization in computer-assisted interventions.
  • This approach overcomes the accuracy limitations and restricted volume of traditional EM tracking.
  • The developed methodology provides a foundation for more accurate and versatile minimally invasive procedures.