Sensitivity regularization of the Cramér‐Rao lower bound to minimize B<sub>1</sub> nonuniformity effects in quantitative magnetization transfer imaging
Bibliographic record
Abstract
Purpose To develop and validate a regularization approach of optimizing B1 insensitivity of the quantitative magnetization transfer (qMT) pool‐size ratio (F). Methods An expression describing the impact of B1 inaccuracies on qMT fitting parameters was derived using a sensitivity analysis. To simultaneously optimize for robustness against noise and B1 inaccuracies, the optimization condition was defined as the Cramér‐Rao lower bound (CRLB) regularized by the B1‐sensitivity expression for the parameter of interest (F). The qMT protocols were iteratively optimized from an initial search space, with and without B1 regularization. Three 10‐point qMT protocols (Uniform, CRLB, CRLB+B1 regularization) were compared using Monte Carlo simulations for a wide range of conditions (e.g., SNR, B1 inaccuracies, tissues). Results The B1‐regularized CRLB optimization protocol resulted in the best robustness of F against B1 errors, for a wide range of SNR and for both white matter and gray matter tissues. For SNR = 100, this protocol resulted in errors of less than 1% in mean F values for B1 errors ranging between −10 and 20%, the range of B1 values typically observed in vivo in the human head at field strengths of 3 T and less. Both CRLB‐optimized protocols resulted in the lowest σF values for all SNRs and did not increase in the presence of B1 inaccuracies. Conclusion This work demonstrates a regularized optimization approach for improving the robustness of auxiliary measurements (e.g., B1) sensitivity of qMT parameters, particularly the pool‐size ratio (F). Predicting substantially less B1 sensitivity using protocols optimized with this method, B1 mapping could even be omitted for qMT studies primarily interested in F.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".