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  Learning-based solution to phase error correction in T2*-weighted GRE scans

Loktyushin, A., Ehses, P., Schölkopf, B., & Scheffler, K. (2018). Learning-based solution to phase error correction in T2*-weighted GRE scans. In International Conference on Medical Imaging with Deep Learning (MIDL 2018) (pp. 1-3).

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-1755-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-1756-4
Genre: Conference Paper

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https://openreview.net/pdf?id=Hkrmm5ioz (Any fulltext)
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 Creators:
Loktyushin, A1, 2, Author              
Ehses, P, Author              
Schölkopf, B3, Author              
Scheffler, K1, 2, Author              
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Long-TE gradient recalled-echo (GRE) scans are prone to phase artifacts due to B0 inhomogeneity. We propose a learning-based approach that does not rely on navigator readouts and allows to infer phase error offsets directly from corrupted data. Our method does not need to be pre-trained on a database of medical images that match a contrast/acquisition protocol of the input image. A sufficient input is a raw multi-coil spectrum of the image that needs to be corrected. We train a convolutional neural network to predict phase offsets for each k-space line of a 2D image. We synthesize training examples online by reconvolving the corrupted spectrum with point spread functions (PSFs) of the coil sensitivity profiles and superimposing artificial phase errors, which we attempt to predict. We evaluate our approach on “in vivo” data acquired with GRE sequence, and demonstrate an improvement in image quality after phase error correction.

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 Dates: 2018-07
 Publication Status: Published online
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Title: International Conference on Medical Imaging with Deep Learning (MIDL 2018)
Place of Event: Amsterdam, The Netherlands
Start-/End Date: 2018-07-04 - 2018-07-06

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Title: International Conference on Medical Imaging with Deep Learning (MIDL 2018)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 3 Identifier: -