Sparse precontrast T<sub>1</sub> mapping for high‐resolution whole‐brain DCE‐MRI
Bibliographic record
Abstract
Purpose To develop and evaluate an efficient precontrast T1 mapping technique suitable for quantitative high‐resolution whole‐brain dynamic contrast‐enhanced–magnetic resonance imaging (DCE‐MRI). Methods Variable flip angle (VFA) T1 mapping was considered that provides 1 × 1 × 2 mm3 resolution to match a recent high‐resolution whole‐brain DCE‐MRI protocol. Seven FAs were logarithmically spaced from 1.5° to 15°. T1 and M0 maps were estimated using model‐based reconstruction. This approach was evaluated using an anatomically realistic brain tumor digital reference object (DRO) with noise‐mimicking 3T neuroimaging and fully sampled data acquired from one healthy volunteer. Methods were also applied on fourfold prospectively undersampled VFA data from 13 patients with high‐grade gliomas. Results T1‐mapping precision decreased with undersampling factor R, althoughwhereas bias remained small before a critical R. In the noiseless DRO, T1 bias was <25 ms in white matter (WM) and <11 ms in brain tumor (BT). T1 standard deviation (SD) was <119.5 ms in WM (coefficient of variation [COV] ~11.0%) and <253.2 ms in BT (COV ~12.7%). In the noisy DRO, T1 bias was <50 ms in WM and <30 ms in BT. For R ≤ 10, T1 SD was <107.1 ms in WM (COV ~9.9%) and <240.9 ms in BT (COV ~12.1%). In the healthy subject, T1 bias was <30 ms for R ≤ 16. At R = 4, T1 SD was 171.4 ms (COV ~13.0%). In the prospective brain tumor study, T1 values were consistent with literature values in WM and BT. Conclusion High‐resolution whole‐brain VFA T1 mapping is feasible with sparse sampling, supporting its use for quantitative DCE‐MRI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".