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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here