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Abstract #2358

A nested iteration artificial neural network approach for efficient high dimensional parameter estimation in 31P-MRF

Mark Widmaier1,2,3, Zirun Wang1, Song-I Lim1,2,3, Daniel Wenz1,2, and Lijing Xin1,2
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 2Animal Imaging and Technology, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland, 3Laboratory of functional and metabolic imaging, Ecole Polytechniqe Federale de Lausanne (EPFL), Lausanne, Switzerland

Synopsis

Keywords: MR Fingerprinting/Synthetic MR, Machine Learning/Artificial Intelligence, MRS, Phosporus, Creatine Kinase, MRFA new magnetization transfer 31P Magnetic Resonance Fingerprinting (31P-MRF) technique is emerging to measure the creatine kinase (CK) chemical exchange rate kCK. The inherent obstacle of the exponential growth in the size of dictionaries with the number of free parameters, was overcome by introducing the nested iteration interpolation method (NIIM). To further reduce the processing time and cope with a nonlinear behaviour, we employed an artificial neuron network (ANN), instead of an interpolation method (IPM) as in the original approach. The nested iteration ANN method (NIAN) is compared with NIIM using simulation data and in vivo 31P-MRF data.

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