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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

You might also read

Related Articles

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

Sort by
Same author

Differences between manufacturer-specified and measured effective inner diameters of vascular introducer sheaths: a micro-CT analysis.

CVIR endovascular·2026
Same author

Genetically engineered bacterial magnetosomes as optimized tracers for magnetic particle imaging.

Acta biomaterialia·2026
Same author

Deep learning for restoring MPI system matrices using simulated training data.

Physics in medicine and biology·2026
Same author

Continuous Coagulation Monitoring in Human Blood Samples via Magnetic Particle Spectroscopy.

International journal of nanomedicine·2026
Same author

Simple-to-Fabricate and Water-Stable Instrument Markers for Preclinical Magnetic Particle Imaging and Magnetic Resonance Imaging.

Medical devices (Auckland, N.Z.)·2026
Same author

The influence of muscle shapes on HDsEMG decomposition yield and accuracy.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·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
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2026

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
07:01

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Published on: June 9, 2016

Model-based reconstruction for magnetic particle imaging.

Tobias Knopp1, Timo F Sattel, Sven Biederer

  • 1Institute of Medical Engineering, University of Lübeck, 23538 Lübeck, Germany. knopp@imt.uni-luebeck.de

IEEE Transactions on Medical Imaging
|May 14, 2009
PubMed
Summary
This summary is machine-generated.

Magnetic particle imaging (MPI) uses a system function for image reconstruction. This study models the system function, enabling faster calibration and potentially reducing memory needs for MPI.

More Related Videos

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement
09:43

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Published on: November 7, 2017

Related Experiment Videos

Last Updated: Jun 23, 2026

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples
07:01

Frequency Mixing Magnetic Detection Scanner for Imaging Magnetic Particles in Planar Samples

Published on: June 9, 2016

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement
09:43

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Published on: November 7, 2017

Area of Science:

  • Medical Imaging
  • Biophysics
  • Nanotechnology

Background:

  • Magnetic particle imaging (MPI) is a novel modality for superparamagnetic nanoparticle imaging.
  • MPI requires a system function for accurate image reconstruction.
  • Current system function acquisition is time-consuming, involving sequential delta sample measurements.

Purpose of the Study:

  • To develop a novel method for calculating the MPI system function.
  • To enable faster and more efficient system function generation for MPI.
  • To explore potential memory reductions in MPI reconstruction.

Main Methods:

  • A model of the signal chain was developed to calculate the system function.
  • The modeled system function was applied to a 1-D MPI experiment.
  • The approach allows for on-the-fly generation of system functions.

Main Results:

  • The modeled system function enabled successful reconstruction of particle distribution in 1-D MPI.
  • This method allows for rapid generation of system functions on dense grids.
  • Potential for reduced memory footprint during reconstruction was identified.

Conclusions:

  • Modeling the MPI signal chain provides a fast alternative to traditional system function calibration.
  • This approach accelerates MPI system function generation and offers computational efficiencies.
  • The method holds promise for improving the practicality and speed of MPI.