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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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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...
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Related Experiment Video

Updated: Oct 15, 2025

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

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Simultaneous Multislice Brain MRI T1 Mapping with Improved Low-Rank Modeling.

Sugil Kim1,2, Suhyung Park3,4

  • 1Siemens Healthineers Korea Ltd., Seoul 03737, Korea.

Tomography (Ann Arbor, Mich.)
|October 26, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for faster magnetic resonance imaging (MRI) parameter quantification, like T1 mapping, using simultaneous multislice (SMS) imaging. The technique effectively reduces artifacts and noise, enabling accurate T1 maps even at high acceleration factors.

Keywords:
T1 mappinglow rankmagnetic resonance imagingnull spacesimultaneous multislice

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Science

Background:

  • Accelerated data acquisition in Magnetic Resonance Imaging (MRI) is crucial for faster scans.
  • Simultaneous Multislice (SMS) imaging allows acquiring multiple slices concurrently, but introduces slice aliasing.
  • Accurate MR parameter quantification, such as T1 mapping, is essential for clinical diagnosis.

Purpose of the Study:

  • To extend the Simultaneous Multislice (SMS) framework for accelerated Magnetic Resonance Imaging (MRI) parameter quantification.
  • To develop a joint reconstruction framework for robust slice separation and recovery in accelerated T1 mapping.
  • To validate the proposed method on human brain scans at high acceleration factors.

Main Methods:

  • Formulated SMS framework as a constrained optimization problem under a joint reconstruction framework.
  • Utilized slice-specific null and signal subspaces for slice separation and recovery.
  • Validated the method on 3T MRI human brain scans, assessing T1 mapping accuracy.

Main Results:

  • The proposed method successfully demonstrated effective slice separation and recovery.
  • Outperformed competing methods in suppressing aliasing artifacts and noise at high SMS accelerations.
  • Generated accurate T1 maps, indicating successful application to accelerated MR parameter quantification.

Conclusions:

  • The extended SMS framework enables accelerated MR parameter quantification, including T1 mapping.
  • The joint reconstruction approach effectively handles slice aliasing and noise in simultaneous multislice imaging.
  • This method offers a promising solution for faster and more accurate MRI scans in clinical settings.