<scp>3D MERMAID</scp> : <scp>3D</scp> Multi‐shot enhanced recovery motion artifact insensitive diffusion for submillimeter, multi‐shell, and <scp>SNR</scp> ‐efficient diffusion imaging
Bibliographic record
Abstract
Abstract Purpose To enhance SNR per unit time of diffusion MRI to enable high spatial resolution and extensive q‐sampling in a feasible scan time on clinical scanners. Methods 3D multi‐shot enhanced recovery motion‐insensitive diffusion (MERMAID) consists of a whole brain nonselective 3D multi‐shot spin‐echo sequence with an inversion pulse immediately before the excitation pulse to enhance the recovery of longitudinal magnetization. The excitation flip angle is reduced to the Ernst angle. The sequence includes a trajectory using radially batched internal navigator echoes (TURBINE) readout, where a 3D projection of the FOV is acquired at a different radial angle in every shot. An image‐based phase‐correction method combined with compressed sensing image reconstruction was developed to correct phase errors between shots. The performance of the 3D MERMAID sequence was investigated using Bloch simulations as well as phantom and human scans at 3 T and then compared to a typical multi‐slice 2D spin‐echo sequence. Results Improvements in SNR per unit time of 70%–240% were observed in phantom and human scans when using 3D MERMAID compared to a single‐slice 2D spin‐echo sequence. This SNR per unit time improvement allowed scans to be acquired at a nominal isotropic resolution of 0.74 mm and a total of 112 directions across four shells ( b = 150, 300, 1000, 2000 s/mm 2 ) in 37 min on a clinical scanner. Conclusion The 3D MERMAID sequence was shown to significantly improve SNR per unit time compared to multi‐slice 2D and 3D diffusion sequences. This SNR improvement allows for shorter scan times and higher spatial and angular resolutions on clinical scanners.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".