363-05-003 ISMRM Abstract

Automated Motion Artifact Check for MRI (AutoMAC-MRI): Explainable severity grading of motion artifacts in MR images

Primary: Analysis Methods - Classification and Prediction

Secondary: Analysis Methods - Visualization

363-05-003 · Classification and Analysis in the Brain · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row D

Keywords: Explainable AI Self-supevised learning Work efficiency Motion Detection Artifact classification

Accepted

Antony Jerald¹, Dattesh Dayanand Shanbhag ¹, SUDHANYA Chatterjee¹

¹GE HealthCare, Bengaluru, India

Presenting Author: Dattesh Dayanand Shanbhag

Synopsis

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References

1. Esses, Steven J., et al. "Automated image quality evaluation of T2‐weighted liver MRI utilizing deep learning architecture." Journal of Magnetic Resonance Imaging 47.3 (2018): 723-728 doi:10.1002/jmri.25779 [doi]

2. Vakli, Pál, et al. "Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics." Medical Image Analysis 88 (2023): 102850. https://doi.org/10.1016/j.media.2023.102850 [doi]

3. Ecker, V., Ganz, M., Eichhorn, H., Marchetto, E., Huelnhagen, T., Yang, B., ... & Küstner, T. "Integrating Inline Quality Control at the MRI Scanner: Global and Local Assessment of Motion Artifacts Using Deep Learning." ISMRM 2025.

4. Chen, Ting, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. "A simple framework for contrastive learning of visual representations." In International conference on machine learning, pp. 1597-1607. PmLR, 2020.

5. Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D. Supervised contrastive learning. Advances in neural information processing systems. 2020;33:18661-73. https://doi.org/10.48550/arXiv.2002.05709 [doi]

6. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016;770–778. doi: 10.1109/CVPR.2016.90 [doi]

7. Maaten, Laurens van der, and Geoffrey Hinton. Visualizing data using t-SNE." Journal of machine learning research 9.Nov (2008): 2579-2605.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/363-05-003