Abstract
PURPOSE: To develop a quantitative multislice chemical exchange saturation transfer (CEST) schedule optimization and pulse sequence that reduces the loss of sensitivity inherent to multislice sequences. METHODS: A deep learning framework was developed for simultaneous optimization of scan parameters and slice order. The optimized sequence was tested in numerical simulations against a random schedule and an optimized single-slice schedule. The scan efficiency of each schedule was quantified. Three healthy subjects were scanned with the proposed sequence. Regions of interest in white matter (WM) and gray matter (GM) were defined. The sequence was compared with the single-slice sequence in vivo and differences quantified using Bland-Altman plots. Test-retest reproducibility was assessed, and the Lin's concordance correlation coefficient (CCC) was calculated for WM and GM. Intersubject variability was also measured with the CCC. Feasibility of whole-brain clinical imaging was tested using a multislab acquisition in 1 subject. RESULTS: The optimized multislice sequence yielded a lower mean error than the random schedule for all tissue parameters and a lower error than the optimized single-slice schedule for four of six parameters. The optimized multislice sequence provided the highest scan efficiency. In vivo tissue-parameter values obtained with the proposed sequence agreed well with those of the optimized single-slice sequence and prior studies. The average WM/GM CCC was 0.8151/0.7779 for the test-retest scans and 0.7792/0.7191 for the intersubject variability experiment. CONCLUSION: A multislice schedule optimization framework and pulse sequence were demonstrated for quantitative CEST. The proposed approach enables accurate and reproducible whole-brain quantitative CEST imaging in clinically relevant scan times.