In Vivo Glutamate CEST MR Fingerprinting (GluCEST-MRF)

Motivation: To obtain quantitative glutamate CEST and MT maps in the brain with higher resolution than spectroscopic imaging. Goal(s): To develop a CEST-MRF pulse sequence and deep learning reconstruction approach for rapid quantitative glutamate imaging. Approach: CEST-MRF pulse sequence with an acquisition schedule optimized by deep learning was developed to measure glutamate exchange rate and volume fractions. Quantitative maps were obtained using a neural network trained on physics-derived signals. Results: The proposed approach yields water T1 and T2 relaxation maps, glutamate exchange and volume fraction maps and the semi-solid exchange and volume fraction maps in a scan time of less than 2 minutes. Impact: The proposed quantitative glutamate-sensitive CEST-MRF technique can lead to improved diagnosis and treatment response evaluation in patients with brain tumors given that glutamate dysregulation is a key aspect of tumor growth.