Optimal <i>k</i>‐space sampling for dynamic contrast‐enhanced MRI with an application to MR renography
Why this work is in the frame
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Bibliographic record
Abstract
For time-resolved acquisitions with k-space undersampling, a simulation method was developed for selecting imaging parameters based on minimization of errors in signal intensity versus time and physiologic parameters derived from tracer kinetic analysis. Optimization was performed for time-resolved angiography with stochastic trajectories (TWIST) algorithm applied to contrast-enhanced MR renography. A realistic 4D phantom comprised of aorta and two kidneys, one healthy and one diseased, was created with ideal tissue time-enhancement pattern generated using a three-compartment model with fixed parameters, including glomerular filtration rate (GFR) and renal plasma flow (RPF). TWIST acquisitions with different combinations of sampled central and peripheral k-space portions were applied to this phantom. Acquisition performance was assessed by the difference between simulated signal intensity (SI) and calculated GFR and RPF and their ideal values. Sampling of the 20% of the center and 1/5 of the periphery of k-space in phase-encoding plane and data-sharing of the remaining 4/5 minimized the errors in SI (<5%), RPF, and GFR (both <10% for both healthy and diseased kidneys). High-quality dynamic human images were acquired with optimal TWIST parameters and 2.4 sec temporal resolution. The proposed method can be generalized to other dynamic contrast-enhanced MRI applications, e.g., MR angiography or cancer imaging.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it