SNU-LIST / QSMnet

Source code for deep neural network trained quantitative susceptibility mapping (QSMnet and QSMnet+)

QSMnet & QSMnet⁺

• The code is for reconstructing Quantitative Susceptibility Mapping by deep neural network (QSMnet) and QSMnet⁺. QSMnet⁺ covers a wider range of susceptibility than QSMnet, using data augmentation approach. Data preprocessing, training and inference code of QSMnet (.py) are availabe.
• The source data for training can be shared to academic institutions. Request should be sent to [e-mail]. For each request, internal approval from our Institutional Review Board (IRB) is required (i.e. takes time).
• Last update : 2022.06.09

References

• QSMnet
  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 Oct;179:199-206. https://www.sciencedirect.com/science/article/pii/S1053811918305378
• QSMnet+
  W. Jung, J. Yoon, S. Ji, J. Choi, J. Kim, Y. Nam, E. Kim, J. Lee. Exploring linearity of deep neural network trained QSM: QSMnet+. Neuroimage. 2020 May; 116619. https://www.sciencedirect.com/science/article/pii/S1053811920301063
  
• Review of deep learning QSM
  W. Jung, S. Bollmann, J. Lee. Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. NMR in Biomedicine. 2020 Mar; e4292. https://doi.org/10.1002/nbm.4292

Overview

(1) QSMnet

(2) QSMnet⁺

Requirements

• Python 3.7

• Tensorflow 1.14.0

• NVIDIA GPU (CUDA 10.0)

• MATLAB R2015b

Data acquisition

• Training data of QSMnet was acquired at 3T MRI (SIEMENS).
• 3D single-echo GRE scan with following sequence parameters: FOV = 256 x 224 x 176 mm³, voxel size = 1 x 1 x 1 x mm³, TR = 33 ms, TE = 25 ms, bandwidth = 100 Hz/pixel, flip angle = 15°.

Manual

First Time Only

1. Clone this repository

git clone https://github.com/SNU-LIST/QSMnet.git

2. Download network
   In Checkpoints directory,

• For Linux User,

sh download_network.sh

• For Windows User,
  https://drive.google.com/drive/folders/1E7e9thvF5Zu68Sr9Mg3DBi-o4UdhWj-8
  and unzip the files in 'Checkpoints/'
  

3. Create conda environment

conda create --name qsmnet -c conda-forge -c anaconda --file requirements.txt 

Phase processing

• Requirements

  • FSL (Ref: S.M. Smith. Fast robust automated brain extraction. Human brain mapping. 2002 Sep;17(3):143-155.)
  • STI Suite (Ref: W. Li, A.V. Avram, B. Wu, X. Xiao, C. Liu. Integrated Laplacian‐based phase unwrapping and background phase removal for quantitative susceptibility mapping. NMR in Biomedicine. 2014 Dec;27(2):219-227.)

• Process flow

  • Extract magnitude and phase image from DICOMs
  • Brain extraction : BET (FSL)
  • Phase unwrapping : Laplaican phase unwrapping (STI Suite)
  • Background field removal : 3D V-SHARP (STI Suite)

• Usage:

  • If you acquired data with different resolution from 1 x 1 x 1 mm³,
    you need to interpolate the data into 1 mm isotropic resolution before phase processing.
    (e.g. zero-padding or truncating in Fourier domain)

  • After 3D V-SHARP in MATLAB, run 'save_input_data_for_QSMnet.m
    '. 'test_input{sub_num}.mat' and 'test_mask{sub_num}.mat' files will be saved in 'Data/Test/Input/'.

    save_input_data_for_QSMnet(TissuePhase, Mask, TE, B0, sub_num)
    % TissuePhase : Results of 3D V-SHARP
    % Mask : Results of 3D V-SHARP
    % TE (s)
    % B0 (T)
    % sub_num : subject number
    % Convert unit from Hz to ppm : field / (Sum(TE) * B0 * gyro) [ppm]

  • Save data with the same orientation and polarity as val_input.mat, val_mask.mat, and val_label.mat files in 'Data/Train/' folder.
  

Training process

• Activate qsmnet conda environment

  conda activate qsmnet 

• Usage

  • Before training, local field & susceptibility maps need to be dividied into 64 x 64 x 64 in Matlab

  python training_data_patch.py
  # PS : Patch size
  # net_name : Network name
  # sub_num : Number of subject to train
  # dir_num : Number of direction per subject
  # patch_num : Number of patches in [x, y, z]

  • Training process in python

  python train.py

Inference

• Activate qsmnet conda environment

  conda activate qsmnet 

• Usage

  python inference.py

  • 'subject#<network_name>-epochs.mat' & 'subject#<network_name>-epochs.nii' will be saved after QSMnet reconstruction.