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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., Herz, K., Glang, F., Dörfler, A., & Zaiss, M. (2022). ComprehenCEST: a clinically feasible CEST protocol to cover all existing CEST preparation schemes by snapshot readout and reduction of overhangs. Poster presented at 24. Jahrestagung der Deutschen Sektion der ISMRM (DS-ISMRM 2022), Aachen, Germany.

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 Creators:
Kamm, L, Author
Fabian, MS, Author
Herz, K1, Author                 
Glang, FM1, Author                 
Dörfler, A, Author
Zaiss, M, Author                 
Affiliations:
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
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.
Materials & Methods
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, B0
and B1 are mapped to contrast via a linear projection, while sparsity-enforcing L1-regularization reduces the number of
consumed offsets (rowLASSO [6]). The training is carried out on uncorrected, raw Z-spectra to generate a selection that
provides robustness against noise and B0/B1 field inhomogeneities. (3) Difference maps between ground truth and model
output are created for volunteer data in the training and validation set.
Results
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).
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: 2022-09
 Publication Status: Published online
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Title: 24. Jahrestagung der Deutschen Sektion der ISMRM (DS-ISMRM 2022)
Place of Event: Aachen, Germany
Start-/End Date: 2022-09-21 - 2022-09-24

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Title: 24. Jahrestagung der Deutschen Sektion der ISMRM (DS-ISMRM 2022)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: P011 Start / End Page: 92 - 93 Identifier: ISBN: 978-3-948023-28-7