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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...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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Related Experiment Video

Updated: Jun 16, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Multi-Modal Iterative Refinement Network with K-space Posterior Correction for MRI reconstruction.

Xin Tang1, Yubao Sun1, Ziyu Sheng1

  • 1School of Computer Science, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Magnetic Resonance Imaging
|June 13, 2026
PubMed
Summary

This study introduces a new AI network for faster Magnetic Resonance Imaging (MRI) reconstruction. The MMIR-Net method improves image quality by aligning multi-modal data and correcting k-space information.

Keywords:
Iterative refinementK-space correctionMRI reconstructionMulti-Modal

Related Experiment Videos

Last Updated: Jun 16, 2026

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Magnetic Resonance Imaging (MRI) is vital for clinical diagnosis but faces challenges with long acquisition times.
  • Undersampling accelerates MRI but causes aliasing artifacts and loss of image detail.
  • Reference-based reconstruction methods use multi-modal data but are limited by spatial misalignment and ignore k-space priors.

Purpose of the Study:

  • To develop an advanced MRI reconstruction method addressing spatial misalignment and k-space limitations.
  • To enhance accelerated MRI reconstruction quality using multi-modal anatomical information.
  • To introduce a novel network for improved Magnetic Resonance Imaging reconstruction.

Main Methods:

  • Proposed the Multi-Modal Iterative Refinement Network with K-space Posterior Correction (MMIR-Net).
  • Developed an Iterative Refinement Network (IR-Net) with a Residual Registration Module (RRM) for progressive alignment.
  • Incorporated a K-space Posterior Correction Module (KPCM) for physical prior correction and an Adaptive Fusion Module (AFM) for domain integration.

Main Results:

  • MMIR-Net demonstrated superior performance compared to existing methods on IXI and fastMRI datasets.
  • The method effectively handles various undersampling patterns and ratios.
  • Achieved enhanced reconstruction quality in accelerated MRI.

Conclusions:

  • The proposed MMIR-Net offers a novel and effective solution for multi-modal MRI reconstruction challenges.
  • MMIR-Net successfully integrates image and k-space information for improved MRI.
  • This work advances accelerated MRI reconstruction by addressing key limitations of current techniques.