Investigating the effect of exchange and multicomponent <i>T</i><sub><i>1</i></sub> relaxation on the short repetition time spoiled steady‐state signal and the DESPOT1 <i>T</i><sub><i>1</i></sub> quantification method
Bibliographic record
Abstract
PURPOSE: To examine the spoiled steady-state (spoiled gradient-recalled echo sequence [SPGR]) signal arising from two-compartment systems and the role of experimental parameters, in particular TR for resolving signal from each compartment. MATERIALS AND METHODS: Using Bloch-McConnell simulations, we examined the SPGR signal from two-component systems in which T(1) is much greater than the mean residence time (tau(m)) of proton spins in each component. Specifically, we examined the role of TR on the ability to resolve each components signal, as well as the influence of experimental parameters on derived DESPOT1 T(1) values. RESULTS: Results revealed that when TR < or = 0.01 tau(m), the measured SPGR signal may be modeled as a summation of signal from each species using a no-exchange approximation. Additionally, under this short TR condition, the driven equilibrium single pulse observation of T(1) (DESPOT1) mapping approach provides T(1) values preferentially biased toward the short or long T(1) species, depending on the choice of flip angles. CONCLUSION: The ability to model the SPGR signal using a no-exchange approximation may permit the quantification multicomponent T(1) relaxation in vivo. Additionally, the ability to preferentially weight the DESPOT1 T(1) value toward the short or long T(1) may provide a useful window into these components.
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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 | 0.007 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".