Optimized MRI contrast for on‐resonance proton exchange processes of PARACEST agents in biological systems
Bibliographic record
Abstract
Image contrast associated with paramagnetic chemical exchange saturation transfer agents can be generated by off-resonance irradiation of agent-bound water or amide protons or on-resonance irradiation of bulk water. Previously, a four-pool model was developed to describe an in vivo system. The model incorporated the magnetization transfer effect from macromolecules when using off-resonance irradiation. In the current study, this four-pool model is modified to describe the in vivo system when using on-resonance irradiation. The influences of pulse power, pulse duration, the chemical shift of bound water, the proton exchange rate between bulk water and bound water, and agent concentration on the on-resonance paramagnetic agent chemical exchange effects were simulated using a WALTZ-16 pulse train in the absence and presence of the macromolecule pool. The results demonstrated that while contrast increases with pulse duration in aqueous solution, there is an optimal pulse duration that maximizes on-resonance paramagnetic agent chemical exchange effects contrast in vivo. This predication was verified by experimental spectroscopic and imaging results from aqueous solution, bovine serum albumin phantoms, and a tissue phantom containing thulium-DOTAM (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetamide)-glycine-lysine. This model can be used to optimize sequence parameters to maximize in vivo on-resonance paramagnetic agent chemical exchange effects contrast.
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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 | 0.000 | 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".