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  Retrospective Motion Correction of Magnitude-Input MR Images

Loktyushin, A., Schuler, C., Scheffler, K., & Schölkopf, B. (2016). Retrospective Motion Correction of Magnitude-Input MR Images. In K. Bhatia (Ed.), Machine Learning Meets Medical Imaging (pp. 3-12). Piscataway, NJ, USA: IEEE.

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 Creators:
Loktyushin, A1, 2, Author           
Schuler, C1, Author           
Scheffler, K2, Author           
Schölkopf, B1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
2Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 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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 Dates: 2016
 Publication Status: Issued
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 Identifiers: DOI: 10.1007/978-3-319-27929-9_1
BibTex Citekey: LoktyushinSSS2016
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Title: First International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), held in conjunction with ICML 2015
Place of Event: Lille, France
Start-/End Date: 2015-07-11

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Title: Machine Learning Meets Medical Imaging
Source Genre: Proceedings
 Creator(s):
Bhatia , K.K., Editor
Lombaert, H., Author
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3 - 12 Identifier: ISBN: 978-3-319-27928-2

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Title: Lecture Notes in Computer Science ; 9487
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