Steady‐state free precession for <scp> T <sub>2</sub> </scp> * relaxometry: All echoes in every readout with k‐space aliasing
Bibliographic record
Abstract
Abstract Purpose Multi‐echo gradient echo imaging is useful for a range of applications including relaxometry, susceptibility mapping, and quantifying relative proportions of fat and water. This relies primarily on long‐TR multi‐echo gradient echo sequences (FLASH), which by design isolate one signal component (i.e., echo) at a time per readout. In this work, we propose an alternative strategy that simultaneously measures all signal components at once in every readout event with an N‐periodic SSFP sequence. Essentially, we Fourier encode the signals into an “F‐k space” similar to the “TE‐k space” of a multi‐echo gradient echo acquisition. This enables an efficient, short‐TR relaxometry experiment where signals benefit from averaging effects over multiple excitations. Theory and Methods In the presented approach, multiple echoes are recorded simultaneously and separated by their differing phase evolution over multiple TRs. At low flip angles the relative echo amplitudes and phases are equivalent to those acquired sequentially from a multi‐echo FLASH, in terms of both T 2 * weighting and spatial phase distributions. The two approaches were compared for the example of R 2 * relaxometry in a phantom and in human volunteers. Results The proposed approach shows close agreement in R 2 * estimation with multi‐echo FLASH, with the advantage of more rapid temporal sampling. Conclusion The proposed approach is a promising alternative to other relaxometry approaches, by measuring signals from multiple echo pathways simultaneously and separating them based on a simple analytical model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".