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Automatic and reference-free MR artifact detection

Visualization of trained network architectures

GUI

easy-to-use graphical interface for medical deep learning

Usage

Install the requirements

$ python3 -m pip install -r requirements.txt

direct

  1. define database layout in config/database/_NAME_OF_DATABASE_.csv (as specified in param.yml -> MRdatabase)
  2. edit parameters in config/param.yml
  3. run code via main.py

GUI

training/prediction can also be invoked from the GUI. Please adapt mainGUI_Template.py according to your needs
Qt_main.py

calling structure

main.py ==> model.fTrain()/fPredict()

Networks

Network Artifact type detection Publication
CNN2D motion_rigid
motion_non-rigid
motion_both
1, 7
CNN3D motion_rigid
motion_non-rigid
motion_both
2, 6
MNetArt motion_rigid
motion_non-rigid
motion_both
2, 4
VNetArt motion_rigid
motion_non-rigid
motion_both
2, 4, 5
DenseNet motion_both
inhomogeneity
noise
DenseResNet motion_both
inhomogeneity
noise
3
ResNet motion_both
inhomogeneity
noise
GoogleNet motion_both
inhomogeneity
InceptionNet motion_both
inhomogeneity
noise
3
VGGNet motion_both
inhomogeneity

References

  1. Küstner, T., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Yang B., Schick F. & Gatidis, S. (2017). Automated reference-free detection of motion artifacts in magnetic resonance images. Magnetic Resonance Materials in Physics, Biology and Medicine, 1-14.
  2. Küstner, T., Jandt, M., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Gatidis, S., Schick, F. & Yang, B. (2018). Automatic Motion Artifact Detection for Whole-Body Magnetic Resonance Imaging. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
  3. Küstner, T., Liu, K., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Yang, B., Schick, F. & Gatidis, S. (2018). Simultaneous detection and identification of MR artifact types in whole-body imaging. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM).
  4. Küstner, T., Jandt, M., Liebgott, A., Mauch, L., Martirosian, P., Bamberg, F., Nikolaou, K., Gatidis, S., Yang, B. & Schick, F. (2018). Motion artifact quantification and localization for whole-body MRI. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM).
  5. Liebgott, A., Milde, S., Jandt, M., Mauch, L., Martirosian, P., Bamberg, F., Schick, F., Nikolaou, K., Yang, B., Gatidis, S. & Küstner, T. (2018). Impact of Labeling Process on Automated Motion Artifact Detection in Whole-Body MR Images with a Deep Learning Approach: A Comparative Study. Proceedings of the ISMRM Workshop on Machine Learning.
  6. Küstner, T., Liegbott, A., Mauch, L., Martirosian, P., Schick, F., Bamberg, F., Nikolaou, K., Yang, B. & Gatidisi, S. (2017). Automatic reference-free motion artifact detection and quantification in T1-weighted MR images of the head and abdomen. Proceedings of the Annual Scientific Meeting (ESMRMB).
  7. Küstner, T., Liebgott, A., Mauch, L., Martirosian, P., Nikolaou, K., Schick, F., Yang, B. & Gatidis, S. (2017). Automatic reference-free detection and quantification of MR image artifacts in human examinations due to motion. Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM).