T<sub>1</sub> and T<sub>2</sub> quantification from standard turbo spin echo images
Bibliographic record
Abstract
Purpose To extract longitudinal and transverse (T 1 and T 2 ) relaxation maps from standard MRI methods. Methods Bloch simulations were used to model relative signal amplitudes from standard turbo spin‐echo sequences: proton density weighted, T 2 ‐weighted, and either T 2 ‐weighted fluid attenuated inversion recovery or T 1 ‐weighted images. Simulations over a range of expected parameter values yielded a look‐up table of relative signal intensities of these sequences. Weighted images and flip angle maps were acquired in 8 subjects at 3 T using both single and multislice acquisitions. The T 1 and T 2 maps were fit by comparing the weighted images to the look‐up table, given the measured flip angles. Results were compared with inversion recovery and multi‐echo spin‐echo experiments. Results A region analysis showed that relaxation maps computed from single‐slice proton density, T 2 and T 1 weighting provided a mean T 1 error of 4% in gray matter and 11% in white matter, and a mean T 2 error of 3% and 4%, respectively, in comparison to reference measurements. In multislice acquisitions that are optimized to reduce cross‐talk and incidental magnetization transfer, the mean T 1 error was 7% in gray matter and 1% in white matter, and the mean T 2 errors were 3% and 4%, respectively. The best T 1 results were achieved using proton density, T 2 and T 1 weighting rather than the fluid attenuated inversion recovery, although T 2 maps were largely unaffected by this choice. Incidental magnetization transfer reduced T 1 accuracy in standard interleaved multislice acquisitions. Conclusion Through exact sequence modeling and separate flip angle measurement, T 2 and T 1 may be quantified from a turbo spin‐echo brain protocol with proton density, T 2 , and T 1 weighting.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".