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Related Concept Videos

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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Atomic Nuclei: Magnetic Resonance01:05

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The number of nuclear spins aligned in the lower energy state is slightly greater than those in the higher energy state. In the presence of an external magnetic field, as the spins precess at the Larmor frequency, the excess population results in a net magnetization oriented along the z axis. When a pulse or a short burst of radio waves at the Larmor frequency is applied along the x axis, the coupling of frequencies causes resonance and flips the nuclear spins of the excess population from the...
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NMR Spectrometers: Resolution and Error Correction01:14

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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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Atomic Nuclei: Nuclear Relaxation Processes01:23

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In the absence of an external magnetic field, nuclear spin states are degenerate and randomly oriented. When a magnetic field is applied, the spins begin to precess and orient themselves along (lower energy) or against (higher energy) the direction of the field. At equilibrium, a slight excess population of spins exists in the lower energy state. Because the direction of the magnetic field is fixed as the z-axis,  the precessing magnetic moments are randomly oriented around the z-axis.
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Applications Of NMR In Biology01:25

Applications Of NMR In Biology

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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.
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Double Resonance Techniques: Overview01:12

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Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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An improved low-rank plus sparse unrolling network method for dynamic magnetic resonance imaging.

Ming-Feng Jiang1, Yun-Jiang Chen1, Dong-Sheng Ruan1

  • 1School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China.

Medical Physics
|November 28, 2024
PubMed
Summary

This study introduces a new deep learning method for dynamic MRI reconstruction, improving efficiency and accuracy by modeling time correlations. The approach achieves better results with fewer parameters, advancing MRI technology.

Keywords:
convolutional long short‐term memory networkdynamic MRI reconstructionlow‐rank decompositiont‐SVDunrolling network

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Area of Science:

  • Medical Imaging
  • Deep Learning
  • Magnetic Resonance Imaging

Background:

  • Deep learning advances dynamic MRI reconstruction.
  • Current methods lack time correlation modeling, reducing efficiency and accuracy.

Purpose of the Study:

  • Develop suitable tensor processing and deep learning models for dynamic MRI.
  • Enhance reconstruction results and reduce network size.

Main Methods:

  • Proposed a novel unrolling network method.
  • Incorporated time correlation modeling using low-rank core matrix and ConvLSTM units.

Main Results:

  • Achieved higher peak signal-to-noise ratios and structural similarity indices.
  • Demonstrated significantly fewer parameters compared to state-of-the-art methods.
  • Evaluated on the AMRG Cardiac MRI dataset at various acceleration factors.

Conclusions:

  • Time correlation modeling is effective for accelerating dynamic MRI reconstruction.
  • The proposed method offers improved performance and efficiency.
  • Serves as a reference for future dynamic MRI reconstruction research.