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

Gach, HM; Song, H; Park, S; Motai, Y; Ruan, D; Liu, W; Stenger, VA; Pohmann, R; Luo, J

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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

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Pohmann, R
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-0000-C4CD-C

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.