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

You might also read

Related Articles

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

Sort by
Same author

Multi-Task Deep Learning on MRI for Tumor Segmentation and Treatment Response Prediction in an Experimental Model of Hepatocellular Carcinoma.

Diagnostics (Basel, Switzerland)·2025
Same author

Combination of Irreversible Electroporation and <i>Clostridium novyi</i>-NT Bacterial Therapy for Colorectal Liver Metastasis.

Cancers·2025
Same author

Current applications of radiomics in the assessment of tumor microenvironment of hepatocellular carcinoma.

Abdominal radiology (New York)·2025
Same author

Therapy Combining Sorafenib and Natural Killer Cells for Hepatocellular Carcinoma: Insights from Magnetic Resonance Imaging and Histological Analyses.

Cancers·2025
Same author

Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer.

International journal of molecular sciences·2024
Same author

Sorafenib plus memory-like natural killer cell immunochemotherapy boosts treatment response in liver cancer.

BMC cancer·2024
Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 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

Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted

Maha Algarawi1,2, Janaki S Saraswatula2, Rajas R Pathare2

  • 1Department of Physics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.

Bioengineering (Basel, Switzerland)
|February 23, 2024
PubMed
Summary
This summary is machine-generated.

A new AI algorithm enhances photomagnetic imaging (PMI) by using magnetic resonance thermometry (MRT) data to improve tumor detection. This AI-driven approach boosts spatial resolution and accuracy while significantly reducing reconstruction time.

Keywords:
artificial intelligenceinverse problemslinear regressionneural networkphoto-magnetic imaging

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

498
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

12.8K

Related Experiment Videos

Last Updated: Jul 2, 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
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

498
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

12.8K

Area of Science:

  • Biomedical Imaging
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Photomagnetic imaging (PMI) combines laser-induced heating with magnetic resonance thermometry (MRT) for temperature and absorption mapping.
  • Tumor detection in PMI relies on temperature contrasts caused by higher hemoglobin levels in abnormal tissues.
  • Existing PMI reconstruction algorithms can be limited in accuracy, spatial resolution, and speed.

Purpose of the Study:

  • To develop and evaluate a novel artificial intelligence-based image reconstruction algorithm for PMI.
  • To improve the accuracy, spatial resolution, and reduce the recovery time of absorption maps in PMI.
  • To leverage machine learning for enhanced tumor boundary detection and functional a priori information in PMI.

Main Methods:

  • A supervised machine learning approach was employed to detect tumor boundaries directly from MRT temperature maps.
  • The detected tumor information was integrated as a soft functional a priori into the standard PMI reconstruction algorithm.
  • The enhanced PMI algorithm was validated using a tissue-like phantom containing inclusions simulating tumors.

Main Results:

  • The AI-enhanced PMI algorithm significantly improved spatial resolution compared to standard methods.
  • Absorption recovery accuracy was enhanced, achieving a low percentage error of 2%.
  • Image artifacts were reduced by 15%, and the reconstruction process was accelerated approximately 9-fold.

Conclusions:

  • The developed AI-based image reconstruction algorithm substantially improves PMI performance.
  • This AI-driven approach offers a more accurate, higher-resolution, and faster method for absorption mapping in biomedical imaging.
  • The findings demonstrate the potential of integrating AI with PMI for improved diagnostic capabilities.