Bibliographic record
Abstract
Standard MRI cine exams for the study of cardiac function are segmented over several heartbeats and thus require a breath-hold to minimize breathing motion artifacts, which is a current limitation of this approach. The purpose of this study was to develop a method for the measurement and correction of respiratory motion that is compatible with cine imaging. Real-time images were used to measure the respiratory motion of heart, to allow translations, rotations, and shears to be measured and corrected in the k-space domain prior to a final gated-segmented reconstruction, using the same data for both purposes. A method for data rejection to address the effects of through-plane motion and complex deformations is described (respiratory gating). A radial k-space trajectory was used in this study to allow direct reconstruction of undersampled real-time images, although the techniques presented are applicable with Cartesian k-space trajectories. Corrected and uncorrected free-breathing gated-segmented images acquired over 18 sec were compared to the current standard breath-hold Cartesian images using both quantitative sharpness profiles (mm(-1)) and clinical scoring (1 to 5 scale, 3: clinically acceptable). Free-breathing, free-breathing corrected, and breath-hold images had average sharpness values of 0.23 +/- 0.04, 0.38 +/- 0.04, and 0.44 +/- 0.04 mm(-1) measured at the blood-endocardium interface, and clinical scores of 2.2 +/- 0.5, 4.2 +/- 0.4, and 4.7 +/- 0.5, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.016 | 0.005 |
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".