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Abstract #3987

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

H Michael Gach1, Hao Song2, Seonyeong Park3, Yuichi Motai3, Dan Ruan4, Wenyang Liu4, V Andrew Stenger5, Rolf Pohmann6, and Jingqin Luo7

1Radiation Oncology and Radiology, Washington University in St Louis, St Louis, MO, United States, 2Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 3Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, United States, 4Radiation Oncology, University of California Los Angeles, Los Angeles, CA, United States, 5Medicine, University of Hawai'i at Manoa, Honolulu, HI, United States, 6High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tubingen, Germany, 7Biostatistics and Medicine, Washington University in St Louis, St Louis, MO, United States

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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