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Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
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Towards predicting the encoding capability of MR fingerprinting sequences.

K Sommer1, T Amthor1, M Doneva1

  • 1Philips Research Europe, Röntgenstr. 24-26, 22335 Hamburg, Germany.

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|July 8, 2017
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A new Monte Carlo method accurately measures magnetic resonance fingerprinting (MRF) sequence performance. This approach aids in optimizing MRF sequences for better imaging capabilities.

Keywords:
Magnetic resonance fingerprintingQuantitative imagingSequence designSequence optimization

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Science

Background:

  • Magnetic Resonance Fingerprinting (MRF) is an advanced quantitative MRI technique.
  • Optimizing MRF sequences is crucial for accurate tissue property mapping but lacks a reliable performance metric.
  • Current methods for evaluating MRF sequence encoding capability are insufficient.

Purpose of the Study:

  • To develop and validate a robust measure for assessing MRF sequence encoding capability.
  • To compare the effectiveness of different candidate measures, including dot-product and Monte Carlo methods.
  • To determine if these measures can predict actual MRF performance in phantom and in vivo studies.

Main Methods:

  • Investigated three candidate measures: local dot-product, global dot-product, and a Monte Carlo noise propagation method.
  • Assessed the consistency of these measures across varying sequence lengths.
  • Validated the predictive power of the measures using phantom and in vivo MRF experiments.

Main Results:

  • Dot-product based measures showed inconsistent results with varying sequence lengths.
  • The Monte Carlo method demonstrated strong agreement with phantom experimental results.
  • The Monte Carlo method accurately predicted the performance of different flip angle patterns in real measurements.

Conclusions:

  • The Monte Carlo method provides a reliable and appropriate measure of MRF sequence encoding capability.
  • This method can be effectively used for optimizing MRF sequence design.
  • The findings advance the development of more accurate quantitative MRI techniques.