A novel method for simultaneous 3D <i>B<sub>1</sub></i> and <i>T<sub>1</sub></i> mapping: the method of slopes (MoS)
Bibliographic record
Abstract
A novel three-dimensional simultaneous B(1) and T(1) mapping method is introduced: the method of slopes (MoS). The linearity of the spoiled gradient recalled echo (SPGR) signal vs flip angle relation is exploited: B(1) mapping is achieved by a two-point extrapolation to signal null with a correction scheme while T(1) mapping uses the slopes of the SPGR signal vs flip angle curves near the origin and near the signal null. This new method improves upon the existing variable flip angle (VFA) T(1)-mapping method in that (i) consistency between B(1) and T(1) maps is ensured (ii) the sampling scheme is T(1)-independent (iii) the noise bias and singularity, associated with using a linear form for the SPGR signal equation, is eliminated by using the full equation. The method is shown to yield accurate and robust results via simulations. Initial estimates of B(1) and T(1) values are obtained from three data points via simple computations and straight line approximations. Initial estimates of B(1) values, for a range of values, are shown to be accurate due to the proposed B(1) correction scheme. The accuracy and robustness of T(1) values is achieved via a non-linear fitting algorithm which includes a fourth data point sampled at high SNR. The MoS was validated by comparing resulting B(1) and T(1) maps with those obtained using other standard methods. Finally, the ability to obtain brain B(1) and T(1) maps using the MoS was demonstrated by in vivo experiments. The MoS is expected to perform well on other motion-free anatomical regions as well.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".