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Artificial neural network for multi-echo gradient echo-based myelin water fraction estimation.

Soozy Jung1, Hongpyo Lee1, Kanghyun Ryu1

  • 1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.

Magnetic Resonance in Medicine
|July 21, 2020
PubMed
Summary
This summary is machine-generated.

An artificial neural network (ANN) improves myelin water fraction (MWF) mapping accuracy using multi-echo gradient-echo signals. This method offers more robust MWF quantification than traditional techniques, enhancing high-resolution imaging.

Keywords:
artificial neural networkmulti-echo gradient echomyelin water imaging

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Myelin water fraction (MWF) is a crucial biomarker for assessing white matter integrity in neurodegenerative diseases.
  • Accurate MWF mapping is essential for reliable diagnosis and monitoring of neurological conditions.
  • Current MWF quantification methods, like nonlinear least-squares, can be sensitive to noise and computationally intensive.

Purpose of the Study:

  • To develop and validate an artificial neural network (ANN) for robust myelin water fraction (MWF) mapping.
  • To utilize multi-echo gradient-echo (GRE) signals for enhanced MWF quantification.
  • To compare the performance of the proposed ANN method against conventional nonlinear least-squares algorithms.

Main Methods:

  • Generated training data for the ANN using simulated multi-echo GRE signals based on a three-pool exponential model.
  • Trained an ANN to accurately estimate MWF from the simulated data.
  • Evaluated the ANN's performance using numerical simulations across various signal-to-noise ratios (SNRs) and analyzed in vivo high-resolution data.
  • Compared ANN-derived MWF values with those obtained from a nonlinear least-squares algorithm.

Main Results:

  • The ANN demonstrated superior accuracy in MWF mapping compared to the nonlinear least-squares method, particularly under noisy conditions.
  • Simulations showed a significant reduction in root-mean-square error (RMS-error) for the ANN (3.56) versus nonlinear least-squares (5.46) at an SNR of 150, representing a 34.80% relative gain.
  • In vivo data analysis revealed reduced standard deviations in region-of-interest analyses, confirming the ANN's robustness and feasibility for high-resolution myelin water imaging.

Conclusions:

  • The developed ANN provides a more robust and accurate method for MWF mapping using multi-echo GRE sequences.
  • The ANN approach overcomes limitations of conventional methods, offering improved performance in challenging imaging conditions.
  • This technique facilitates the acquisition of high-resolution myelin water images, advancing neuroimaging capabilities.