<i>k‐t</i> BLAST and <i>k‐t</i> SENSE: Dynamic MRI with high frame rate exploiting spatiotemporal correlations
Bibliographic record
Abstract
Dynamic images of natural objects exhibit significant correlations in k-space and time. Thus, it is feasible to acquire only a reduced amount of data and recover the missing portion afterwards. This leads to an improved temporal resolution, or an improved spatial resolution for a given amount of acquisition. Based on this approach, two methods were developed to significantly improve the performance of dynamic imaging, named k-t BLAST (Broad-use Linear Acquisition Speed-up Technique) and k-t SENSE (SENSitivity Encoding) for use with a single or multiple receiver coils, respectively. Signal correlations were learned from a small set of training data and the missing data were recovered using all available information in a consistent and integral manner. The general theory of k-t BLAST and k-t SENSE is applicable to arbitrary k-space trajectories, time-varying coil sensitivities, and under- and overdetermined reconstruction problems. Examples from ungated cardiac imaging demonstrate a 4-fold acceleration (voxel size 2.42 x 2.52 mm(2), 38.4 fps) with either one or six receiver coils. k-t BLAST and k-t SENSE are applicable to many areas, especially those exhibiting quasiperiodic motion, such as imaging of the heart, the lungs, the abdomen, and the brain under periodic stimulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".