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WaveSep

This repo contains the official PyTorch implementation for the paper WaveSep: A Flexible Wavelet-based Approach for Source Separation in Susceptibility Imaging, at MLCN 2023

by Zhenghan Fang, Hyeong-Geol Shin, Peter van Zijl, Xu Li, and Jeremias Sulam

Dependencies

Create and activate a new conda environment

conda create -n wavesep python==3.10
conda activate wavesep

Install necessary python packages

pip install -r requirements.txt

Usage

QSM source separation

python wavesep/qsm_sep.py --data <yml of input data>

The yml file contains the input data for QSM source separation. See data/yml/template_qsm.yml for more details. See data/yml/example_qsm.yml for an example.

🔄 Update (2025/04/23): Support for different Dr values for para- and dia-magnetic maps (Dr_pos ≠ Dr_neg)

In this case, the second term in fQSM in Eq. (3) in the paper is changed from

1/2 * || R2' / Dr - (x_pos - x_neg) ||_2^2

to

1/2 * || R2' / Dr_pos - (x_pos - x_neg * Dr_neg / Dr_pos) ||_2^2.

where the units are:

  • R2': Hz
  • Dr, Dr_pos, Dr_neg: Hz/ppm

STI source separation

python wavesep/sti_sep.py --data <yml of input data>

The yml file contains the input data for STI source separation. See data/yml/template_sti.yml for more details. See data/yml/example_sti.yml for an example.

Contact

If you have any questions, please contact me at

Zhenghan Fang

Email: zfang23@jhu.edu