Temporal stability of adaptive 3D radial MRI using multidimensional golden means
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breast tumor diagnosis requires both high spatial resolution to obtain information about tumor morphology and high temporal resolution to probe the kinetics of contrast uptake. Adaptive sampling of k-space allows images in dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) to be reconstructed at various spatial or temporal resolutions from the same dataset. However, conventional radial approaches have limited flexibility that restricts image reconstruction to predetermined resolutions. Golden-angle radial k-space sampling achieves flexibility in-plane with samples that are incremented by the golden angle, which fills two-dimensional (2D) k-space with radial spokes that have a relatively uniform angular distribution for any time interval. We extend this method to three-dimensional (3D) radial sampling, or 3D-Projection Reconstruction (3D-PR) using multidimensional golden means, which are derived from modified Fibonacci sequences by an eigenvalue approach. We quantitatively compare this technique to conventional 3D radial methods in terms of the fluctuation in error caused by undersampling artifacts, and show that the golden 3D-PR method can substantially improve the temporal stability of quantitative measurements made from dynamic images when compared to conventional 3D radial approaches of k-space sampling.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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