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Abstract Purpose To shorten CEST acquisition time by leveraging Z‐spectrum undersampling combined with deep learning for CEST map construction from undersampled Z‐spectra. Methods Fisher information gain analysis identified optimal frequency offsets (termed “Fisher offsets”) for the multi‐pool fitting model, maximizing information gain for the amplitude and the FWHM parameters. These offsets guided initial subsampling levels. A U‐NET, trained on undersampled brain CEST images from 18 volunteers, produced CEST maps at 3 T with varied undersampling levels. Feasibility was first tested using retrospective undersampling at three levels, followed by prospective in vivo undersampling (15 of 53 offsets), reducing scan time significantly. Additionally, glioblastoma grade IV pathology was simulated to evaluate network performance in patient‐like cases. Results Traditional multi‐pool models failed to quantify CEST maps from undersampled images (structural similarity index SSIM <0.2, peak SNR <20, Pearson r <0.1). Conversely, U‐NET fitting successfully addressed undersampled data challenges. The study suggests CEST scan time reduction is feasible by undersampling 15, 25, or 35 of 53 Z‐spectrum offsets. Prospective undersampling cut scan time by 3.5 times, with a maximum mean squared error of 4.4e–4, r = 0.82, and SSIM = 0.84, compared to the ground truth. The network also reliably predicted CEST values for simulated glioblastoma pathology. Conclusion The U‐NET architecture effectively quantifies CEST maps from undersampled Z‐spectra at various undersampling levels.
Cheema et al. (Fri,) studied this question.