Abstract
There has been a considerable progress recently in understanding and developing solutions to the problem of image quality deterioration due to patients’ motion in MR scanners. Retrospective methods can be applied to previously acquired motion corrupted data, however, such methods require complex-valued raw volumes as input. It is common practice, though, to preserve only spatial magnitudes of the medical scans, which makes the existing post-processing-based approaches inapplicable. In this work, we make first humble steps towards solving the problem of motion-related artifacts in magnitude-only scans. We propose a learning-based approach, which involves using large-scale convolutional neural networks to learn the transformation from motion-corrupted magnitude observations to the sharp images.
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Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B. (2015). Retrospective Motion Correction of Magnitude-Input MR Images. In: Bhatia, K., Lombaert, H. (eds) Machine Learning Meets Medical Imaging. MLMMI 2015. Lecture Notes in Computer Science(), vol 9487. Springer, Cham. https://doi.org/10.1007/978-3-319-27929-9_1
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DOI: https://doi.org/10.1007/978-3-319-27929-9_1
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