Partitioning <i>k</i>‐space for cylindrical three‐dimensional rapid acquisition with relaxation enhancement imaging in the mouse brain
Why this work is in the frame
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Bibliographic record
Abstract
Three-dimensional rapid acquisition with relaxation enhancement (RARE) scans require the assignment of each phase encode step in two dimensions to an echo in the echo train. Although this assignment is frequently made across the entire Cartesian grid, collection of only the central cylinder of k-space by eliminating the corners in each phase encode dimension reduces the scan time by ~22% with negligible impact on image quality. The recipe for the assignment of echoes to grid points for such an acquisition is less straightforward than for the simple full Cartesian acquisition case, and has important implications for image quality. We explored several methods of partitioning k-space-exploiting angular symmetry in one extreme or emulating a cropped Cartesian acquisition in the other-and acquired three-dimensional RARE magnetic resonance imaging (MRI) scans of the ex vivo mouse brain. We evaluated each partitioning method for sensitivity to artifacts and then further considered strategies to minimize these through averaging or interleaving of echoes and by empirical phase correction. All scans were collected 16 at a time with multiple-mouse MRI. Although all schemes considered could be used to generate images, the results indicate that the emulation of a standard Cartesian echo assignment, by partitioning preferentially along one dimension within the cylinder, is more robust to artifacts. Samples at the periphery of the bore showed larger phase deviations and higher sensitivity to artifacts, but images of good quality could still be obtained with an optimized acquisition protocol. A protocol for high-resolution (40 μm) ex vivo images using this approach is presented, and has been used routinely with a success rate of 99% in over 1000 images.
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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 | 0.001 | 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