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Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction.

Hongjiang Wei1, Steven Cao2, Yuyao Zhang3

  • 1Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

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|August 5, 2019
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Summary
This summary is machine-generated.

A new deep learning method, autoQSM, directly estimates magnetic susceptibility from MRI phase images, improving accuracy for brain edge tissues without complex preprocessing steps.

Keywords:
Deep learningMRIMagnetic resonance imagingNeural networkQSMQuantitative susceptibility mapping

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Computational Neuroscience

Background:

  • Quantitative susceptibility mapping (QSM) reconstructs tissue magnetic susceptibility from MRI data.
  • Traditional QSM methods require extensive preprocessing, leading to inaccuracies at brain tissue edges.

Purpose of the Study:

  • To develop a learning-based QSM method (autoQSM) for direct magnetic susceptibility estimation.
  • To overcome limitations of existing QSM techniques, particularly near brain boundaries.

Main Methods:

  • A modified U-net neural network was trained using QSM maps from a two-step method.
  • The network directly estimates susceptibility from total phase images, bypassing brain extraction and background removal.
  • Training involved 209 healthy subjects; validation included diverse datasets like mouse brains and lesioned brains.

Main Results:

  • AutoQSM accurately recovers magnetic susceptibility in regions near brain edges, including cortical veins and superficial structures.
  • The method demonstrates robustness and generalization to unseen data, including pathological cases.
  • Comparisons show autoQSM produces high-quality maps with improved speed and reduced preprocessing requirements.

Conclusions:

  • AutoQSM offers a streamlined and accurate approach to QSM, enhancing visualization of superficial neuroanatomy.
  • The method's efficiency and accuracy show significant potential for clinical and research applications in neuroimaging.