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 Experiment Videos

Array compression for MRI with large coil arrays.

Martin Buehrer1, Klaas P Pruessmann, Peter Boesiger

  • 1Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology, Zurich, Switzerland.

Magnetic Resonance in Medicine
|May 31, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Prospective Head Motion Correction in T1- and T2-Weighted Long Echo Train Sequences Using Servo Navigation.

Magnetic resonance in medicine·2026
Same author

Direct MRI of collagen.

eLife·2026
Same author

The sinking dynamics of a solid intruder in concentrated cornstarch suspensions studied using ultra-fast magnetic resonance imaging.

Soft matter·2026
Same author

Core-shell particles with tailored magnetic susceptibility for signal-efficient magnetic resonance imaging of granular systems.

Journal of magnetic resonance (San Diego, Calif. : 1997)·2026
Same author

Autonomy for MRI Field Cameras: Synchronization, Self-Calibration, and Sequence Detection.

Magnetic resonance in medicine·2026
Same author

Motion- and Field-Robust Mesoscopic Whole-Brain <math><semantics><mrow><msubsup><mi>T</mi> <mn>2</mn> <mo>*</mo></msubsup></mrow> <annotation>$$ {T}_2^{\ast } $$</annotation></semantics></math> -Weighted Imaging at 7 and 11.7 T Using Servo Navigation.

Magnetic resonance in medicine·2026
Same journal

A Comparison of Tissue Property Values Estimated Using Conventional Cardiac MRF and MT-Cardiac MRF.

Magnetic resonance in medicine·2026
Same journal

Dependence of the Extra-Cellular Diffusion Coefficient on the Fractions of Neurites and Cell Bodies in Gray Matter.

Magnetic resonance in medicine·2026
Same journal

Triple-Pulse <sup>23</sup>Na MRI Sequence (TriNa) for Simultaneous Acquisition of Spin-Density-Weighted and Fluid-Attenuated Images.

Magnetic resonance in medicine·2026
Same journal

Evaluation of Phantom Doping Materials in Quantitative Susceptibility Mapping.

Magnetic resonance in medicine·2026
Same journal

Design of an 8-Channel Transmit 32-Channel Receive 11.7T Head Coil and Evaluation of SNR Gains.

Magnetic resonance in medicine·2026
Same journal

The Potential for Absolute Temperature Imaging Based on Brain Metabolites Using an FID-Shifting Approach in Gradient Echo Planar Spectroscopic Imaging (GREPSI).

Magnetic resonance in medicine·2026
See all related articles

Coil array compression reduces data from many MRI channels to fewer, lowering computational load. This method achieves significant compression with minimal signal-to-noise ratio (SNR) loss, enhancing MRI reconstruction efficiency.

Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Imaging Technology
  • Biomedical Engineering

Background:

  • Large-element coil arrays in MRI offer enhanced signal-to-noise ratios (SNRs) and parallel imaging capabilities.
  • Processing extensive data from numerous independent MRI channels presents significant computational and memory challenges during image reconstruction.

Purpose of the Study:

  • To introduce and evaluate a novel method called coil array compression for MRI data processing.
  • To reduce the computational and memory burden associated with large-channel-count MRI systems.

Main Methods:

  • Coil array compression involves combining datasets from independent channels in the time domain before image reconstruction.
  • The method's effectiveness was assessed based on the size of the region of interest (ROI).

Related Experiment Videos

  • 2D in vivo cardiac data acquired with a 32-element phased-array coil was used for evaluation.

    • Main Results:

    • Coil array compression effectively reduced the number of channels, for instance, from 32 to four elements.
    • Minimal signal-to-noise ratio (SNR) loss of only 0.3% was observed in a heart-focused ROI.
    • With twofold parallel imaging, a compression factor of 8 resulted in a mere 2% SNR loss.

    Conclusions:

    • Coil array compression is a highly effective technique for managing large MRI datasets.
    • The method significantly reduces computational load with negligible impact on image quality (SNR).
    • This approach holds promise for improving the efficiency of MRI reconstruction, particularly with advanced coil arrays.