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Abstract #0865

Deep Learning-Based Respiratory Motion Correction in Free-Breathing Abdominal Diffusion-Weighted Imaging

Jinho Kim1,2,3, Fasil Gadjimuradov2,3, Thomas Benkert3, Thomas Vahle3, and Andreas Maier2
1Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany, 2Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany, 3MR Application Pre-development, Siemens Healthcare GmbH, Erlangen, Germany


Since a single diffusion-weighted image can suffer from low SNR, multiple DWI repetitions can be averaged to improve SNR, which, however, can introduce blurring due to respiratory motion between different repetitions. Consequently, retrospective gating can be performed to overcome this problem. However, conventional retrospective gating has low SNR efficiency as it discards parts of the data and may result in certain slices to be missing for the desired motion state. This work proposes an efficient Deep Learning-based motion-correction method to improve conventional retrospective gating in free-breathing DWI, resulting in sharper images while maintaining image information from all acquired repetitions.

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