Multislice <i>T</i><sub>1</sub>‐prepared 2D single‐shot EPI: analysis of a clinical <i>T</i><sub>1</sub> mapping method unbiased by <i>B</i><sub>0</sub> or <i>B</i><sub>1</sub> inhomogeneity
Bibliographic record
Abstract
Quantitative MR imaging is as sensitive in detecting lesions as qualitative imaging, but it is potentially more specific in differentiating disease. T1 mapping in particular might help to assess acute ischemic stroke, multiple sclerosis, epilepsy and Alzheimer's disease better. Thus, a rapid and robust clinical technique is vital. In 1990, Ordidge and colleagues developed the multislice T1 -prepared two-dimensional (2D) single-shot echo planar imaging technique. Subsequent studies demonstrated its clinical viability, but none performed an in-depth analysis of the strengths and advantages of this T1 mapping method. Herein, theoretical and experimental evidence shows that the technique accounts for 2D slice profile effects and is unbiased by B0 or B1 inhomogeneity. This is verified explicitly by varying the linear shims, the T1 preparation flip angle and the excitation flip angle. Furthermore, it is shown that the repetition time (and hence scan time) can be reduced without a loss of T1 accuracy. Copyright © 2016 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".