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Quantitative susceptibility mapping using deep neural network: QSMnet.

Jaeyeon Yoon1, Enhao Gong2, Itthi Chatnuntawech3

  • 1Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.

Neuroimage
|June 13, 2018
PubMed
Summary
This summary is machine-generated.

A new deep learning algorithm, QSMnet, reconstructs high-quality MRI susceptibility maps from single-orientation data. This method overcomes limitations of previous techniques, offering improved image quality and consistency for potential clinical applications.

Keywords:
DipoleMRIMachine learningMagnetic susceptibilityQSMReconstruction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Deep neural networks show promise in medical image reconstruction (CT, PET, MRI).
  • Quantitative Susceptibility Mapping (QSM) is crucial for restoring magnetic susceptibility from MRI field maps.
  • Existing QSM methods (COSMOS, TKD, MEDI) face challenges with data acquisition or image artifacts.

Purpose of the Study:

  • Develop a novel MRI reconstruction algorithm using deep neural networks for Quantitative Susceptibility Mapping (QSM).
  • Address limitations of current QSM techniques by enabling high-quality susceptibility map generation from single-orientation data.

Main Methods:

  • A modified U-net deep neural network (QSMnet) was designed for QSM.
  • The network was trained using gold-standard COSMOS QSM maps and augmented data.
  • Extensive datasets (35 images) were used for training, validation, and testing.

Main Results:

  • QSMnet demonstrated superior image quality compared to TKD and MEDI methods.
  • QSMnet achieved image quality comparable to the COSMOS gold standard.
  • QSMnet maps showed significantly better consistency across multiple head orientations than TKD or MEDI.

Conclusions:

  • QSMnet effectively generates high-quality susceptibility maps from single-orientation MRI data.
  • The algorithm overcomes limitations of previous QSM methods, offering improved image quality and consistency.
  • Preliminary tests on patient data suggest QSMnet's potential for clinical applications in neuroimaging.