Modeling <sup>1</sup>H exchange: An estimate of the error introduced in MRI by assuming the fast exchange limit in bolus tracking
Bibliographic record
Abstract
A simulation is presented which calculates the MRI signal expected from a model tissue for a given pulse sequence after a bolus injection of a contrast agent. The calculation assumes two physiologic compartments only, the intravascular and extravascular spaces. The determination of the concentration of contrast in each compartment as a function of time and position has been outlined in a previous publication (Moran and Prato, Magn Reson Med 2001;45:42-45). These contrast agent concentrations are used here to determine the NMR relaxation times as a function of time and position within the tissue. Knowledge of this simulated tissue 'map' of relaxation times as a function of time provides the information required to determine whether the proton exchange rate is fast or slow on the NMR timescale. Since with a bolus injection the concentration of contrast and hence the relaxation time may vary with position along the capillary, some segments of the capillary are allowed to be in fast exchange with the extravascular space, while others may be in slow exchange. Using this information, and parameters specific to a given tissue, the MRI signal for a given pulse sequence is constructed which correctly accounts for differences in proton exchange across the length of the capillary. It is shown that extravascular contrast agents show less signal dependence on water exchange, and thus may be more appropriate for quantitative imaging when using fast exchange assumptions. It is also shown that nondistributed compartment models can incorrectly estimate the water exchange that is occurring at the capillary level if exchange-minimizing pulse sequences are not used.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".