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Abstract #0676

Accelerating the B0 Inhomogeneity Correction for GluCEST Imaging Using Deep Learning

Yiran Li1, Danfeng Xie1, Abigail Cember2, Ravi Prakash Reddy Nanga2, Hanlu Yang1, Dushyant Kumar2, Hari Hariharan2, Li Bai1, John A. Detre3, Ravinder Reddy2, and Ze Wang4
1Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA, United States, 2Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States, 3Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States, 4Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States

Glutamate Chemical Exchange Saturation Transfer (GluCEST) MRI is a noninvasive technique for mapping parenchymal glutamate in the brain. GluCEST signal is sensitive to magnetic field (B0) inhomogeneity. Corrections for B0 inhomogeneity often require repeated data acquisitions at several saturation offset frequencies, which however dramatically prolongs the total acquisition time and can cause practical issues such as increased sensitive to patient motions. Another technique challenge in GluCEST MRI is the low signal-to-noise-ratio (SNR) as the signal is derived from the small z-spectrum difference. Both issues were addressed in this study with a novel deep learning-based algorithm armed with wide activation neurons.

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