Adaptive bilateral breast MRI using projection reconstruction time‐resolved imaging of contrast kinetics
Bibliographic record
Abstract
PURPOSE: To introduce a bilateral implementation of an adaptive imaging technique in which both dynamic and high resolution breast MR images are acquired simultaneously. MATERIALS AND METHODS: Adaptive three-dimensional bilateral breast imaging in the sagittal plane was achieved by combining two elements: a projection reconstruction time-resolved imaging of contrast kinetics (PR-TRICKS) k-space trajectory and a slab interleaved sequence that imaged alternate breasts every TR. A pilot study was performed to evaluate image quality and contrast uptake behavior, using eight patients with previously identified benign lesions. RESULTS: Adaptive reconstruction demonstrated breast lesions in all eight women with similar image quality and signal-to-noise ratio (SNR) to Cartesian images with comparable imaging parameters. Contrast enhancement curves covering the entire postinjection time period were obtained from the dynamic images and in one case compared to previous enhancement profiles from a conventional Cartesian trajectory. CONCLUSION: Bilateral dynamic and high spatial resolution images with high SNR can be achieved in a clinically feasible manner, providing both kinetic and morphologic analysis with a single data set. This may obviate the need for multiple MRI examinations for a thorough breast MRI workup.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".