Contribution of Tissue Harmonic Imaging and Frequency Compound Imaging in Interventional Breast Sonography
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to retrospectively compare conventional imaging, frequency compound imaging (CI), and tissue harmonic imaging (THI) in interventional breast sonography. METHODS: Institutional Review Board approval and patient informed consent were not required. The authors reviewed 104 sonographically guided breast procedures in 83 patients. For each biopsy, 4 images obtained with conventional imaging, frequency CI at 10 and 14 MHz (CI10 and CI14), and THI were graded independently by 2 radiologists for lesion conspicuity, needle conspicuity, lesion and needle conspicuity, and overall image quality. Frequency CI at 10 MHz, CI14, and THI were compared with conventional imaging. Different clinical scenarios (fatty versus glandular background, fine needle versus core needle, and oblique versus horizontal needle direction) were evaluated. RESULTS: Statistical analysis showed that for overall image quality, CI10 was the best setting (odds ratios [OR], 3.67 and 7.48). For lesion conspicuity, CI14 (OR, 3.55) and THI (OR, 1.77) improved lesion visibility in a fatty background, whereas THI (OR, 0.26) was very limited in a glandular background. For needle conspicuity, no setting was better than conventional, whereas THI was the least valuable setting (OR, 0.011 and 0.049). For lesion and needle conspicuity, CI10 showed significantly better results than conventional for a dense background (P = .0268 and .4028; OR, 2.435 and 1.383) with 1 reviewer, whereas THI was the least valuable setting (OR, 0.014 and 0.042). CONCLUSIONS: Conventional imaging provided the best assessment of lesion and needle conspicuity. Frequency compounding is a useful setting for dense breast and for fine-needle aspiration. Tissue harmonic imaging has a role in the visualization of a lesion against a fatty background but is of limited value in needle visualization.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".