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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.
