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Abstract #5112

Accelerated CEST Imaging with Parallel Deep Convolutional Neural Networks

Huajun She1, Shu Zhang1, Xinzeng Wang1, Ece Ercan1, Jochen Keupp2, Anath Madhuranthakam1,3, Ivan Dimitrov1,4, Robert Lenkinski1,3, and Elena Vinogradov1,3

1Radiology, UT Southwestern Medical Center, Dallas, TX, United States, 2Philips Research, Hamburg, Germany, 3Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States, 4Philips Healthcare, Gainesville, FL, United States

CEST is a new contrast mechanism in MRI. However, a successful application of CEST is hampered by its slow acquisition. This work investigates accelerating CEST imaging using parallel convolutional neural networks (PCNN). We extend the Cascade-CNN into a multi-channel model and train the network establish a mapping from the multi-coil input to multi-coil output. This work is the first try to apply deep learning and convolutional neural networks technique in accelerating CEST imaging. The in vivo brain results show that the proposed method demonstrates a high quality reconstruction of the MTRasym maps with different saturation pulses at R=4.

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