Cucurbit[6]uril Hyperpolarized Chemical Exchange Saturation Transfer Pulse Sequence Parameter Optimization and Detectability Limit Assessment at 3.0T
Bibliographic record
Abstract
Abstract Molecular imaging is the future of personalized medicine; however, it requires effective contrast agents. Hyperpolarized chemical exchange saturation transfer (HyperCEST) can boost the signal of Hyperpolarized 129Xe MRI and render it a molecular imaging modality of high efficiency. Cucurbit[6]uril (CB6) has been successfully employed in vivo as a contrast agent for HyperCEST MRI, however its performance in a clinical MRI scanner has yet to be optimized. In this study, MRI pulse sequence parameter optimization was first performed in CB6 solutions in phosphate‐buffered saline (PBS), and subsequently in whole sterile citrated bovine blood. The performance of four different depolarization pulse shapes (sinusoidal, 3‐lobe sinc (3LS), rectangular (block), and hyperbolic secant (hypsec) was optimized. The detectability limits of CB6 in a clinical 3.0T MRI scanner was assessed using the optimized pulse sequences. The 3LS depolarization pulses performed best, and demonstrated 24 % depletion in a 25 μM solution of CB6 in PBS. It performed similarly in blood. The CB6 detectability limit was found to be 100 μM in citrated bovine blood with a correspondent HyperCEST depletion of 30 % ±9 %. For the first time, the HP 129Xe HyperCEST effect was observed in red blood cells (RBC) and had a similar strength as HyperCEST in plasma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".