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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