Supine breast MRI
Bibliographic record
Abstract
PURPOSE: To achieve high-quality unilateral supine breast magnetic resonance imaging (MRI) as a step to facilitate image aiding of clinical applications, which are often performed in the supine position. Contrast-enhanced breast MRI is a powerful tool for the diagnosis of cancer. However, prone patient positioning typically used for breast MRI hinders its use for image aiding. MATERIALS AND METHODS: A fixture and a flexible four-element receive coil were designed for patient-specific shaping and placement of the coil in close conformity to the supine breast. A 3D spoiled gradient sequence was modified to incorporate compensation of respiratory motion. The entire setup was tested in volunteer experiments and in a pilot patient study. RESULTS: The flexible coil design and the motion compensation produced supine breast MR images of high diagnostic value. Variations in breast shape and in tissue morphology within the breast were observed between a supine and a diagnostic prone MRI of a patient. CONCLUSION: The presented supine breast MRI achieved an image quality comparable to diagnostic breast MRI. Since supine positioning is common in many clinical applications such as ultrasound-guided breast biopsy or breast-conserving surgery, the registration of the supine images will aid these applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".