Rapid high‐resolution <i>T</i><sub>1</sub> mapping by variable flip angles: Accurate and precise measurements in the presence of radiofrequency field inhomogeneity
Bibliographic record
Abstract
Rapid 3D mapping of T(1) relaxation times is valuable in diverse clinical applications. Recently, the variable flip angle (VFA) spoiled gradient recalled echo approach was shown to be a practical alternative to conventional methods, providing better precision and speed. However, the method is known to be sensitive to transmit field (B(1) (+)) inhomogeneity and can result in significant systematic errors in T(1) estimates, especially at high field strengths. The main challenge is to improve the accuracy of the VFA approach without sacrificing speed. In this article, the VFA method was optimized for both accuracy and precision by considering the influence of imperfect transmit fields, noise bias, and selection of flip angles. An analytic solution was developed for systematic B(1) (+)-induced T(1) errors and allows simple correction of T(1) measurements acquired with any imaging parameters. A noise threshold was also identified and provided a guideline for avoiding T(1) biases. Finally, it was shown that three flip angles were the most efficient for maintaining accuracy and high precision over large ranges of T(1). A rapid B(1) (+) mapping sequence was employed in all phantom experiments and high-field in vivo brain scans. Experimental results confirmed the theory and validated the accuracy of the proposed method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".