Accelerating parameter mapping with a locally low rank constraint
Bibliographic record
Abstract
PURPOSE: To accelerate MR parameter mapping using a locally low rank (LLR) constraint, and the combination of parallel imaging and the LLR constraint. THEORY AND METHODS: An LLR method is developed for MR parameter mapping and compared with a globally low rank method in a multiecho spin-echo T2 mapping experiment. For acquisition with coil arrays, a combined LLR and parallel imaging method is proposed. The proposed method is evaluated in a variable flip angle T1 mapping experiment and compared with the LLR method and parallel imaging alone. RESULTS: In the multiecho spin-echo T2 mapping experiment, the LLR method is more accurate than the globally low rank method for acceleration factors 2 and 3, especially for tissues with high T2 values. Variable flip angle T1 mapping is achieved by acquiring datasets with 10 flip angles, each dataset accelerated by a factor of 6, and reconstructed by the proposed method with a small normalized root mean square error of 0.025. CONCLUSIONS: The LLR method is likely superior to the globally low rank method for MR parameter mapping. The proposed combined LLR and parallel imaging method has better performance than the two methods alone, especially with highly accelerated acquisition.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".