Tissue Harmonic Imaging, Frequency Compound Imaging, and Conventional Imaging
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate different sonographic settings (tissue harmonic, frequency compounding, and conventional imaging) and to determine which setting optimizes breast lesion detection and lesion characterization. METHODS: Four hundred thirteen consecutive breast lesions (249 benign and 164 malignant) were evaluated by sonography using 4 different modes (conventional imaging at 14 MHz, tissue harmonic imaging at 14 MHz [THI], and frequency compound imaging at 10 MHz [CI10] and 14 MHz [CI14]). The images were reviewed by consensus by 2 breast radiologists. For each image, the lesion was graded for conspicuity, mass margin assessment, echo texture assessment, overall image quality, and posterior acoustic features. RESULTS: For lesion conspicuity, THI and CI14 were better than conventional imaging (P < .01) and CI10 (P < .01) particularly against a fatty background (P < .01 for THI versus conventional for a fatty background versus P = .13 for a dense background). Frequency compound imaging at 10 MHz performed the best in echo texture assessment (P < .01), as well as overall image quality (P < .01). For margin assessment, CI10 performed better for deep and large (> or =1.5-cm) lesions, whereas CI14 performed better for small (<1.5-cm) and superficial lesions. Finally, THI and CI14 increased posterior shadowing (P < .01) and posterior enhancement (P < .01). CONCLUSIONS: The standard breast examination incorporates 2 distinct processes, lesion detection and lesion characterization. With respect to detection, THI is useful, especially in fatty breasts. With respect to characterization, compound imaging improves lesion echo texture assessment. No single setting in isolation can provide the necessary optimized information for both of these tasks. As such, a combination approach is best.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".