Poster — Wed Eve—01: The Characterization of Tissue Harmonic Ultrasound Imaging for Potential Use in Prostate Brachytherapy
Bibliographic record
Abstract
A phantom has been designed and fabricated to explore the potential benefits of tissue harmonic ultrasound imaging (THI) in prostate brachytherapy. Transverse and sagittal images of the phantom were acquired using 6, 9 and 12 MHz ultrasound frequencies in brightness (B) mode and 10 and 12 MHz in THI mode. The imaging parameters such as dead zone, depth of penetration, geometrical accuracy, axial and lateral resolution, contrast resolution and signal to noise ratio (SNR) in B‐mode were compared with those in THI‐mode. It was found that the dead zone is 1mm at all frequencies of both B‐mode and THI‐mode. The depth of penetration decreases insignificantly in THI mode, while contrast resolution and SNR improves. The axial resolution in THI mode (12 MHz) is ∼45% higher than B‐mode (6 MHz). The lateral resolution in THI mode (12 MHz) is ∼50% higher in the near zone and ∼10% higher in the far field than corresponding values in B‐mode (6 MHz). In general harmonic images are clearer, less noisy and display fewer artifacts than B‐mode images. This phantom study demonstrates that THI‐mode has the potential to provide better images of the prostate, which may help in prostate volume identification and improved treatment planning. An improvement in target localization has a potential to improve the treatment outcomes and reduction in the treatment related complications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".