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χ-sepnet: Deep Neural Network for Magnetic Susceptibility Source Separation.

Minjun Kim1, Sooyeon Ji1,2, Jiye Kim1

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

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Summary

We developed deep learning pipelines for magnetic susceptibility source separation, significantly reducing artifacts in brain imaging. The method accurately identifies paramagnetic and diamagnetic sources, even in multiple sclerosis lesions, with potential for broad clinical use.

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Quantitative Susceptibility Mapping (QSM) methods like magnetic susceptibility source separation (χ-separation) estimate brain's paramagnetic and diamagnetic sources.
  • Conventional QSM suffers from streaking artifacts and requires time-consuming data acquisition for accurate susceptibility mapping.
  • Existing methods face challenges in resolving susceptibility distributions and require extensive imaging protocols.

Purpose of the Study:

  • To develop novel deep learning-based pipelines for artifact reduction and improved accuracy in susceptibility source separation.
  • To propose two pipelines: χ-sepnet- (using GRE and spin-echo data) and χ-sepnet- (using GRE data only).
  • To evaluate the performance of these pipelines against conventional methods in healthy subjects and multiple sclerosis patients.

Main Methods:

  • Development of a deep learning network, χ-sepnet, trained on streaking artifact-free data from multiple head orientations.
  • Implementation of two pipelines: χ-sepnet- and χ-sepnet- , utilizing multi-echo GRE and optional multi-echo spin-echo data.
  • Qualitative and quantitative assessments in healthy subjects and analysis of lesion characteristics in multiple sclerosis patients.

Main Results:

  • The proposed deep learning pipelines generated high-quality χ-separation maps with substantially reduced artifacts compared to conventional methods.
  • χ-sepnet- and χ-sepnet- demonstrated superior performance in quantitative analysis, outperforming regularization-based reconstruction.
  • Accurate classification of multiple sclerosis lesions into paramagnetic and diamagnetic subtypes (99.6% and 98.4% concordance, respectively).

Conclusions:

  • Deep learning-based susceptibility source separation pipelines (χ-sepnet) offer significant improvements in image quality and artifact reduction.
  • The χ-sepnet- pipeline, requiring only multi-echo GRE data, shows great potential for widespread clinical and scientific applications.
  • Further evaluation is warranted for diverse diseases and pathological conditions to fully establish clinical utility.