Combining chemical exchange saturation transfer and<sup>1</sup>H magnetic resonance spectroscopy for simultaneous determination of metabolite concentrations and effects of magnetization exchange
Bibliographic record
Abstract
Purpose A new sequence combining chemical‐exchange saturation‐transfer (CEST) with traditional MRS is used to simultaneously determine metabolite content and effects of magnetization exchange. Methods A CEST saturation block consisting of a train of RF‐pulses is placed before a metabolite‐cycled semi‐LASER single‐voxel spectroscopy sequence. The saturation parameters are adjustable to allow optimization of the saturation for a specific target. Data were collected in brain from 20 subjects in experiments with different B 1 ‐settings (0.4‐2.0 µT) on a 3T MR scanner. CEST Z‐spectra were calculated from water intensities and fitted with a multi‐pool Lorentzian model. Interrelated metabolite spectra were fitted in fitting tool for arrays of interrelated datasets (FiTAID). Results Evaluation of traditional Z‐spectra from water revealed exchange effects from amides, amines, and hydroxyls as well as an upfield nuclear Overhauser effect. The magnetization transfer effect was evaluated on metabolites and macromolecules for the whole spectral range and for the different B 1 levels. A correction scheme for direct saturation on metabolites is proposed. Both magnetization‐transfer and direct saturation proved to differ for individual metabolites. Conclusion Using non‐water‐suppressed spectroscopy offers time‐saving simultaneous recording of the traditional CEST Z‐spectrum from water and the metabolite spectrum under frequency‐selective saturation. In addition, exchange and magnetization‐transfer effects on metabolites and macromolecules can be detected, which might offer additional possibilities for quantification or give further insight into the composition of the traditional CEST Z‐spectrum. Apparent magnetization‐transfer effects on macromolecular signals in the 1 H‐MR spectrum have been found. Detailed knowledge of magnetization‐transfer effects is also relevant for judging the influence of water‐suppression on the quantification of metabolite signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".