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  Performance comparison of artificial neural network and fuzzy deep learning algorithms for respiratory motion prediction in pseudocontinuous arterial spin labeling of the abdomen

Gach, H., Song, H., Park, S., Motai, Y., Ruan, D., Liu, W., et al. (2017). Performance comparison of artificial neural network and fuzzy deep learning algorithms for respiratory motion prediction in pseudocontinuous arterial spin labeling of the abdomen. Poster presented at 25th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2017), Honolulu, HI, USA.

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Gach, HM, Author
Song, H, Author
Park, S, Author
Motai, Y, Author
Ruan, D, Author
Liu, W, Author
Stenger, VA, Author
Pohmann, R1, 2, Author           
Luo, J, 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              

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 Abstract: Subtraction-based imaging methods like pseudocontinuous arterial spin labeling (pCASL) in the body are challenging due to physiological motion. Respiratory motion prediction (RMP) using an artificial neural network (ANN) and pencil beam navigators was previously integrated into a pCASL sequence to permit free-breathing perfusion MRI of the kidney. In an effort to improve the accuracy of the RMP, we compared the performance of a promising fuzzy deep learning (FDL) algorithm with ANN using navigator-echo displacements recorded from 8 volunteers during pCASL. FDL combines ANN with fuzzy logic. However, the ANN performance was significantly better than FDL for the pCASL application.

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 Dates: 2017-04-25
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: GachSPMRLSPL2017
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Title: 25th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2017)
Place of Event: Honolulu, HI, USA
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Title: 25th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2017)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 3987 Start / End Page: - Identifier: -