SNU-LIST/chi-separation

Code for magnetic susceptibility source separation

⭐New toolbox is now available for test!⭐

chi-separation toolbox (χ-separation, x-separation)

• The GUI-based MATLAB toolbox including algorithms for magnetic susceptibility source separation based on convex optimization (χ-separation or chi-separation; H. Shin et al., Neuroimage, 2021) and deep learning-based reconstruction. The toolbox also supports phase preprocessing (e.g. phase unwrapping and background removal) powered by MEDI, STI Suite, SEGUE, and mritools toolboxs (see the Chisep_script.m file for details).

• The χ-separation toolbox includes the following features:

  • DICOM/NIFTI data compatibility
  • QSMnet: Quantitative susceptibility mapping (QSM) reconstruction algorithm based on deep neural network (QSMnet; J. Yoon et al., Neuroimage, 2018)
  • χ-separation using R₂' (or R₂* ): Magnetic susceptibility source separation algorithms based on convex optimization (χ-separation; H. Shin et al., Neuroimage, 2021) that share similar contrasts and optimization parameters with either MEDI+0 (Liu et al., MRM, 2018) or iLSQR (Li et al., Neuroimage, 2015) algorithms. The toolbox also provides the option to use pseudo R₂ map if R₂ measurement is not availabe (using R₂' is reconmmanded for accurate estimation).
  • χ-sepnet using R₂' (or R₂* ): A U-Net-based neural network that reconstructs COSMOS-quality χ-separation using R₂' and phase. In case R₂ is not measured, another neural network is trained to estimate χ-separation maps from R₂* and phase.

• Last update: June-11-2024 (Sooyeon Ji, Hyeong-Geol Shin, Jun-Hyeok Lee, Minjoon Kim, Kyeongseon Min)

Reference

• H. Shin, J. Lee, Y. H. Yun, S. H. Yoo, J. Jang, S.-H. Oh, Y. Nam, S. Jung, S. Kim, F. Masaki, W. Kim, H. J. Choi, J. Lee. χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain. Neuroimage, 2021 Oct; 240:118371.
• J. Yoon, E. Gong, I. Chatnuntawech, B. Bilgic, J. Lee, W. Jung, J. Ko, H. Jung, K. Setsompop, G. Zaharchuk, E.Y. Kim, J. Pauly, J. Lee. Quantitative susceptibility mapping using deep neural network: QSMnet. Neuroimage. 2018;179:199-206

Requirements

• Common

  • MATLAB (tested in R2019a-R2021a)

• Additional

  • For QSMnet and χ-sepnet, Deep Learning MATLAB Toolbox Converter for ONNX Model Format (https://www.mathworks.com/matlabcentral/fileexchange/67296-deep-learning-toolbox-converter-for-onnx-model-format)
  • For DICOM/NIFTI read and phase processing, see https://www.dropbox.com/sh/3zafav50bfnruuu/AABVVYpdsznsRXKy8YKK4ybla?dl=0

Contacts

• snu.list.software@gmail.com
• sin4109@gmail.com (Hyeong-Geol Shin, PhD)