Spatial Subspace Methods for Dynamic Magnetic Resonance Imaging Reconstruction
Bibliographic record
Abstract
Data acquisition in magnetic resonance imaging (MRI) is a slow process. Despite being an over- whelmingly safe modality with excellent tissue contrast and diagnostic ability, long and expensive scans prevent its widespread clinical uptake. Current solutions to this problem operate by acquiring less data than typically required for MRI image reconstruction and developing techniques to estimate the unacquired data. In this work, we propose a new approach to reconstructing dynamic MRI data (i.e., MRI video of anatomy over time). Our new approach can be generally summarized by first esti- mating a spatial subspace from data acquired during the scan of interest (i.e., no prior training data required), then applying the spatial subspace to estimate the final reconstruction. This works well because the number of coefficients to estimate for spatial subspace reconstruction is usually small enough to enable a over/well-determined reconstruction problem, given that the spatial subspace is well estimated. This is not the case for common temporal subspace methods, for which the number of coefficients to estimate is large, and the problem is usually underdetermined. We apply our new approach to dynamic contrast-enhanced (DCE) MRI, MR Fingerprinting (MRF), and CINE MRI. In each case, we compare our spatial subspace methods to the gold standard and show the relative improvement in reconstruction quality of our methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".