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  comprehenCEST: a clinically feasible CEST protocol to cover all existing CEST preparation schemes by snapshot readout and reduction of overhangs

Kamm, L., Fabian, M., Glang, F., Herz, K., & Zaiss, M. (2022). comprehenCEST: a clinically feasible CEST protocol to cover all existing CEST preparation schemes by snapshot readout and reduction of overhangs. In 9th International Workshop on Chemical Exchange Saturation Transfer Imaging (CEST 2022) (pp. 56).

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Kamm, L, Author
Fabian, M, Author
Glang, F1, Author           
Herz, K1, Author           
Zaiss, M1, Author           
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1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              

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 Abstract: INTRODUCTION: CEST sequences preparing a selected metabolite normally sample the full Z-spectrum, allowing for asymmetry or model- based evaluations. To achieve good labelling efficiency for various metabolites, different physical preparations are required. Sparsity-enforcing machine learning algorithms help to select and combine the differently CEST-prepared images from a sequence pool covering the preparation parameter space, while preserving main contrast information. Together with a fast, single-shot 3D readout, we create a 3D CEST protocol containing 13 established contrasts in 10 minutes scan time. METHODS: (1) 13 contrasts are evaluated conventionally (PCA-denoising, dB0-correction, MTR asymmetry or Lorentz fit) from six existing CEST sequences [2-5], which cover B1cwpe levels from 0.5 to 4 uT, offering the ground truth. Mz is prepared using standardized pulseq-CEST building blocks [7-8], and probed with the fast snapshotCEST 3D readout [1]. (2) All Z-spectra are mapped to contrast via a linear projection, while sparsity-enforcing L1-regularization reduces the number of consumed offsets (rowLASSO [6,10]). The training is carried out on uncorrected, raw Z-spectra to generate a selection that provides robustness against noise and B0/B1 inhomogeneities. (3) Difference maps between ground truth and model output are created for the validation dataset. RESULTS: 5 of the 13 generated CEST maps are shown in Figure 1. Lowering CEST offsets down to 82 still yields similar imaging contrast. The normalized, mean absolute error (NMAE) between linear model and ground truth, averaged over all 13 contrasts, and for retaining offset rate r is: 27 ± 6% (r = 1), 29 ± 7% (r = 0.8), 32 ± 8% (r = 0.6), 36 ± 9% (r = 0.4), 42 ± 11% (r = 0.2). Residual errors visible here might still originate from an observed B0 drift during the whole data acquisition; it is excepted that this can be further improved. DISCUSSION & CONCLUSION: Instead of arguing which is the best CEST protocol to provide new insights into a pathology, and only measure one CEST contrast, we suggest measuring them all. By combining sparse sampling and snapshot readout, a comprehensive protocol covering most of the reported labellings of 10 minutes is conceivable. This allows to design powerful hypotheses generating clinical pilot studies.

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 Dates: 2022-08
 Publication Status: Published online
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Title: 9th International Workshop on Chemical Exchange Saturation Transfer Imaging (CEST 2022)
Place of Event: Atlanta, GA, USA
Start-/End Date: 2022-08-07 - 2022-08-10

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Title: 9th International Workshop on Chemical Exchange Saturation Transfer Imaging (CEST 2022)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 56 Identifier: -