T<sub>2</sub> quantification in brain using 3D fast spin‐echo imaging with long echo trains
Bibliographic record
Abstract
Abstract Purpose Three‐dimensional fast spin‐echo (FSE) sequences commonly use very long echo trains (>64 echoes) and severely reduced refocusing angles. They are increasingly used in brain exams due to high, isotropic resolution and reasonable scan time when using long trains and short interecho spacing. In this study, T2 quantification in 3D FSE is investigated to achieve increased resolution when comparing with established 2D (proton‐density dual‐echo and multi‐echo spin‐echo) methods. Methods The FSE sequence design was explored to use long echo trains while minimizing T2 fitting error and maintaining typical proton density and T2‐weighted contrasts. Constant and variable flip angle trains were investigated using extended phase graph and Bloch equation simulations. Optimized parameters were analyzed in phantom experiments and validated in vivo in comparison to 2D methods for eight regions of interest in brain, including deep gray‐matter structures and white‐matter tracts. Results Phantom and healthy in vivo brain T2 measurements showed that optimized variable echo‐train 3D FSE performs similarly to previous 2D methods, while achieving three‐fold‐higher slice resolution, evident visually in the 3D T2 maps. Optimization resulted in better T2 fitting and compared well with standard multi‐echo spin echo (within the 8‐ms confidence limits defined based on Bland‐Altman analysis). Conclusion T2 mapping using 3D FSE with long echo trains and variable refocusing angles provides T2 accuracy in agreement with 2D methods with additional high‐resolution benefits, allowing isotropic views while avoiding incidental magnetization transfer effects. Consequently, optimized 3D sequences should be considered when choosing T2 mapping methods for high anatomic detail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".