Characterization of ultrasound elevation beamwidth artifacts for prostate brachytherapy needle insertion
Bibliographic record
Abstract
PURPOSE: Ultrasound elevation beamwidth leads to image artifacts and uncertainties in localizing objects (such as a surgical needle) in ultrasound images. The authors examined the clinical significance of errors caused by elevation beamwidth artifacts and imaging parameters in needle insertion procedures. METHODS: Beveled prostate brachytherapy needles were inserted through all holes of a grid template under real-time transrectal ultrasound (TRUS) guidance. The needle tip position as indicated by the TRUS image was compared to their observed physical location. A new device was developed to measure the ultrasound elevation beamwidth. RESULTS: Imaging parameters of the TRUS scanner have direct impact on the localization error ranging from 0.5 up to 4 mm. The smallest localization error was observed laterally close to the center of the grid template and axially within the beam's focal zone. Largest localization error occurs laterally around both sides of the grid template and axially within the beam's far field. The authors also found that the localization errors vary with both lateral and elevation offsets. CONCLUSIONS: The authors found properly adjusting the TRUS imaging settings to lower the ultrasound gain and power effectively minimized the appearance of elevation beamwidth artifacts and in turn reduced the localization errors of the needle tip.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".