Investigating exchange and multicomponent relaxation in fully‐balanced steady‐state free precession imaging
Bibliographic record
Abstract
PURPOSE: To investigate the effect of chemical exchange and multicomponent relaxation on the rapid T(2) mapping method, DESPOT2 (driven equilibrium single pulse observation of T(2)) and the steady-state free precession (SSFP) sequence upon which it is based. Although capable of rapid T(2) determination, an assumption implicit of the method is single-component relaxation. In many biological tissues (such as white and gray matter), it is well established that the T(2) decay curve is more accurately described by the summation of more than one relaxation species. MATERIALS AND METHODS: The effects of exchange were first incorporated into the general SSFP magnetization expressions and its effect on the measured SSFP signal investigated using Bloch-McConnell simulations. Corresponding imaging experiments were performed to support the presented theory. RESULTS: Simulations show the measured multicomponent SSFP signal may be expressed as a linear summation of signal from each species under usual imaging conditions where the repetition time is much less than T(2). Imaging experiments performed using dairy cream demonstrate strong agreement with the presented theory. Finally, using a dairy cream model, we demonstrate quantification of multicomponent relaxation from multiangle SSFP data for the first time, showing good agreement with reference spin-echo values. CONCLUSION: SSFP and DESPOT2 may provide a new method for investigating multicomponent systems, such as human brain, and disease processes, such as multiple sclerosis.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".