DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification
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Purpose In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi-pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use. Methods We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B-1 inhomogeneities. The input data for a neural feed-forward network consisted of 7 T in vivo uncorrected Z-spectra of a single B-1 level, and a B-1 map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel-wise to target data consisting of Lorentzian amplitudes generated conventionally by 5-pool Lorentzian fitting of normalized, denoised, B-0- and B-1-corrected Z-spectra. The deepCEST network was trained with Gaussian negative log-likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes. Results The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B-1 level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably. Conclusion The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B-0- and B-1-corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.
Original language | English |
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Journal | Magnetic Resonance in Medicine |
Volume | 89 |
Issue number | 4 |
Pages (from-to) | 1543-1556 |
ISSN | 0740-3194 |
DOIs | |
Publication status | Published - 2023 |
- amide, CEST, deep learning, neural networks, rNOE, uncertainty quantification, SATURATION, PROVIDES
Research areas
ID: 327068579