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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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Pattern recognition for rapid T2 mapping with stimulated echo compensation.

Chuan Huang1, Maria I Altbach2, Georges El Fakhri1

  • 1Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.

Magnetic Resonance Imaging
|May 24, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a faster pattern recognition method for T2 mapping using the extended phase graph (SEPG) model. The new technique accurately compensates for indirect echoes, significantly reducing computation time for T2 and B1 maps.

Keywords:
Indirect echoesMR parameterQuantitative MRISlice imperfectionStimulated echoesT2 mapping

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Area of Science:

  • Magnetic Resonance Imaging
  • Quantitative MRI
  • Biomedical Engineering

Background:

  • Indirect echoes contaminate multi-echo spin-echo T2 quantification, leading to overestimation with conventional models.
  • Nonlinear least square fitting of the extended phase graph (SEPG) model offers accurate T2 estimates but is computationally intensive.
  • Current methods for T2 map generation are time-consuming due to iterative fitting processes.

Purpose of the Study:

  • To develop a computationally efficient pattern recognition technique for T2 mapping based on the SEPG model.
  • To enable accurate T2 and B1 map generation with reduced computation time.
  • To provide a method applicable to any arbitrary echo spacing.

Main Methods:

  • A pattern recognition approach utilizing a pre-computed dictionary based on the SEPG signal model was developed.
  • The technique was designed to compensate for indirect echo contamination.
  • In vivo data was used to validate the proposed method against conventional iterative nonlinear least square fitting.

Main Results:

  • The pattern recognition technique achieved almost identical T2 and B1 maps compared to conventional iterative methods.
  • Computation time was reduced by over 14-fold using the proposed pattern recognition approach.
  • The method demonstrated effectiveness for in vivo quantitative MRI.

Conclusions:

  • The developed pattern recognition T2 mapping technique significantly accelerates computation time while maintaining accuracy.
  • This method offers a viable alternative for rapid and accurate T2 and B1 quantification in MRI.
  • The SEPG-based pattern recognition approach overcomes the computational limitations of iterative fitting methods.