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

5.1K
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...
5.1K

You might also read

Related Articles

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

Sort by
Same author

Vasopressin type-2 receptor mRNA expressions in endolymphatic sac and temporal bone cT findings in Meniere's disease.

Acta oto-laryngologica·2026
Same author

Super-resolution deep learning reconstruction improves the depiction of peripancreatic arteries and image quality in pancreatic cancer CT.

Abdominal radiology (New York)·2026
Same author

Evaluation of super-resolution deep learning reconstruction on three-dimensional constructive interference in steady state for enhanced visualization of vestibular schwannomas.

Radiological physics and technology·2026
Same author

Improved delineation of the cystic artery using super-resolution deep learning reconstruction in contrast-enhanced abdominal computed tomography.

Radiological physics and technology·2026
Same author

Image Distortion Correction for Diffusion MR Imaging Using a Transformer-based U-Net.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine·2026
Same author

3D quantitative synthetic MRI for assessing Alzheimer's clinical syndrome, subjective cognitive impairment, and mild cognitive impairment.

Neuroradiology·2026

Related Experiment Video

Updated: Jun 22, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.4K

Iterative Motion Correction Technique with Deep Learning Reconstruction for Brain MRI: A Volunteer and Patient Study.

Koichiro Yasaka1,2, Hiroyuki Akai2,3, Shimpei Kato3

  • 1Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.

Journal of Imaging Informatics in Medicine
|June 28, 2024
PubMed
Summary

Iterative motion correction (IMC) significantly reduces motion artifacts in brain MRI scans reconstructed with deep learning reconstruction (DLR). This technique improves overall image quality without compromising similarity to motionless scans.

Keywords:
Artificial intelligenceBrainDeep learningMagnetic resonance imagingMotion artifact

More Related Videos

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain
06:52

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Published on: January 26, 2024

1.9K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K

Related Experiment Videos

Last Updated: Jun 22, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.4K
Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain
06:52

Author Spotlight: Advancing 3D Cytoarchitecture Analysis - Rapid Volumetric Reconstruction of the Human Brain

Published on: January 26, 2024

1.9K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.6K

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Motion artifacts are a common problem in brain MRI, degrading image quality and potentially affecting diagnosis.
  • Deep learning reconstruction (DLR) offers advanced image reconstruction but can still be susceptible to motion-induced artifacts.
  • Iterative motion correction (IMC) is a technique aimed at mitigating these artifacts.

Purpose of the Study:

  • To evaluate the effectiveness of iterative motion correction (IMC) in reducing motion artifacts in brain MRI.
  • To assess the impact of IMC on image quality when combined with deep learning reconstruction (DLR).
  • To compare quantitative and qualitative measures of image quality with and without IMC.

Main Methods:

  • Brain MRI scans (FLAIR sequence) were acquired from 10 volunteers (motionless and self-induced motion) and 30 patients.
  • Images were reconstructed using deep learning reconstruction (DLR) with and without iterative motion correction (IMC).
  • Quantitative analysis used the Structural Similarity Index Measure (SSIM); qualitative analysis involved blinded reader evaluation of motion artifacts, noise, and overall quality.

Main Results:

  • Quantitative analysis showed a significantly higher SSIM for IMC-on images (0.952) compared to IMC-off images (0.949) (p < 0.001).
  • Qualitative analysis revealed that all three blinded readers found motion artifacts and overall image quality to be significantly better with IMC-on (p < 0.001).
  • While two readers noted increased noise with IMC-on (p < 0.001), the reduction in motion artifacts and improvement in overall quality were deemed more significant.

Conclusions:

  • Iterative motion correction (IMC) is effective in reducing motion artifacts in brain FLAIR DLR images.
  • IMC preserves the similarity of reconstructed images to motionless scans.
  • The combination of IMC and DLR enhances overall image quality in brain MRI, making it a valuable technique for clinical practice.