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

5.2K
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...
5.2K
Applications Of NMR In Biology01:25

Applications Of NMR In Biology

3.7K
Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
3.7K
Proteomics01:33

Proteomics

7.3K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.3K

You might also read

Related Articles

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

Sort by
Same author

Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.

Protein science : a publication of the Protein Society·2026
Same author

Correlated clustering and projection for dimensionality reduction.

Machine learning: science and technology·2026
Same author

VARIANT: Web Server for Decoding and Analyzing Viral Mutations at Genome and Protein Levels.

ArXiv·2026
Same author

Manifold topological deep learning for biomedical data.

Nature communications·2026
Same author

A review of recent advances in generative artificial intelligence models for biomolecular sciences.

Acta pharmaceutica Sinica. B·2026
Same author

CAP: Commutative algebra prediction of protein-nucleic acid binding affinities.

Machine learning: science and technology·2026

Related Experiment Video

Updated: Jul 3, 2025

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

9.6K

Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.

Saumya Nigam1,2, Elvira Gjelaj1,3, Rui Wang4

  • 1Precision Health Program, Michigan State University, East Lansing, Michigan, USA.

Journal of Magnetic Resonance Imaging : JMRI
|February 15, 2024
PubMed
Summary

Magnetic Particle Imaging (MPI) offers high sensitivity and resolution. This review explores how machine learning and deep learning enhance MPI image reconstruction and analysis for future advancements.

Keywords:
artificial intelligencedeep learningmachine learningmagnetic particle imaging

More Related Videos

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

2.7K
Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics
07:42

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

Published on: February 19, 2017

8.8K

Related Experiment Videos

Last Updated: Jul 3, 2025

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

9.6K
Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

2.7K
Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics
07:42

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

Published on: February 19, 2017

8.8K

Area of Science:

  • Medical Imaging
  • Biophysics
  • Artificial Intelligence

Background:

  • Magnetic Particle Imaging (MPI) is a developing technique offering high sensitivity and spatial resolution.
  • MPI provides 2D/3D imaging with high temporal resolution, non-ionizing radiation, and excellent contrast.
  • Traditional MPI image reconstruction relies on system matrices and X-space methods.

Purpose of the Study:

  • To review the application and significance of machine learning (ML) and deep learning (DL) in MPI.
  • To highlight the potential of AI-driven methods for improving MPI image reconstruction and analysis.
  • To summarize current research trends in AI for MPI.

Main Methods:

  • Review of existing literature on ML and DL applications in MPI.
  • Analysis of AI-based approaches for MPI signal processing and image reconstruction.
  • Discussion of the integration of AI into the MPI workflow.

Main Results:

  • AI, particularly ML and DL, shows significant promise for enhancing MPI image quality and reconstruction.
  • These methods can address limitations in traditional MPI reconstruction techniques.
  • The review identifies key areas where AI is making an impact in MPI research.

Conclusions:

  • Machine learning and deep learning are crucial for advancing Magnetic Particle Imaging.
  • AI-driven techniques are poised to improve the precision and utility of MPI.
  • Future research should focus on further developing and validating AI models for MPI applications.