An <scp>RF</scp> breast coil for 0.2<scp>T</scp><scp>MRI</scp>
Bibliographic record
Abstract
ABSTRACT Breast cancer is the most frequently diagnosed cancer in women. High field studies have shown the diagnostic value of breast MRI, but the examination costs greatly exceed those of competing conventional mammography. Low field MRI offers typical MRI contrast at substantially lower cost, but has suffered from lower spatial resolution. Specificity of breast MRI can potentially be increased by acquiring MR imaging with higher spatial or temporal resolution, but the signal‐to‐noise ratio (SNR) achievable in a given imaging time becomes limiting. SNR for the particular pulse sequence and magnet field strength is strongly influenced by the characteristics of the radio‐frequency coil. An optimal breast coil should yield excellent SNR but also generate a homogeneous B1 field, while allowing imaging of the both breasts simultaneously and maintaining patient comfort. RF receiver coil design is a key determinant of image quality, thus to address this we have designed and constructed a low field breast imaging coil. The coil was tested with a 4‐post 0.2T MRI providing high quality breast images. Designed and constructed saddle rf coil allows to obtain good quality image of the breast using low 0.2 T MRI system within 2 minutes. The coil provides patient comfort as breast compression is not required and minimizes artefacts caused by respiration or motion. A high contrast, low‐cost and pain‐free breast examination using optimized low field MRI system has the potential to serve a large patient population for whom current technologies have deficiencies. © 2015 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 46B: 3–7, 2016
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".