Improved motion correction capabilities for fast spin echo <i>T</i><sub>1</sub> FLAIR propeller using non‐Cartesian external calibration data driven parallel imaging
Bibliographic record
Abstract
Patient motion is a common challenge in the clinical setting and fast spin echo longitudinal relaxation time fluid attenuating inversion recovery imaging method with motion correction would be highly desirable. The motion correction provided by transverse relaxation time- and diffusion-weighted periodically rotated overlapping parallel lines with enhanced reconstruction methods has seen significant clinical adoption. However, periodically rotated overlapping parallel lines with enhanced reconstruction with fast spin echo longitudinal relaxation time fluid attenuating inversion recovery-weighting has proved challenging since motion correction requires wide blades that are difficult to acquire while also maintaining short echo train lengths that are optimal for longitudinal relaxation time fluid attenuating inversion recovery-weighting. Parallel imaging provides an opportunity to increase the effective blade width for a given echo train lengths. Coil-by-coil data-driven autocalibrated parallel imaging methods provide greater robustness in the event of motion compared to techniques relying on accurate coil sensitivity maps. However, conventional internally calibrated data-driven parallel imaging methods limit the effective acceleration possible for each blade. We present a method to share a single calibration dataset over all imaging blades on a slice by slice basis using the APPEAR non-cartesian parallel imaging method providing an effective blade width increase of 2.45×, enabling robust motion correction. Results comparing the proposed technique to conventional cartesian and periodically rotated overlapping parallel lines with enhanced reconstruction methods demonstrate a significant improvement during subject motion and maintaining high image quality when no motion is present in normal and clinical volunteers.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".