Artifact-Resilient Needle Visualisation in Prostate Brachytherapy Using Angular Plane Wave Compounding and Confidence-Weighted Imaging
Bibliographic record
Abstract
Precise needle visualisation is essential for accurate radioactive seed placement in prostate brachytherapy. However, imaging with transrectal ultrasound probes is challenged by artifacts such as specular reflection, reverberation, and multiple needle images. These artifacts degrade both tissue and needle visualisation and can compromise procedural accuracy.In this work, we present a novel imaging strategy that combines angular plane wave compounding across multiple steering angles with confidence map-weighted image reconstruction. The confidence map adaptively weighs angular acquisitions based on local signal reliability, enabling suppression of spurious reflections while preserving the needle’s cross-section in the images. Experimental validation is performed using both a water tank and an anatomically realistic prostate phantom, with imaging conducted with multiple needles present in the imaging plane simultaneously. Performance was quantitatively evaluated using the Full Width at Half Maximum of the needle’s lateral profile as an objective measure of visibility and localisation precision, alongside reverberation peak analysis to assess artifact suppression. The proposed confidence-weighted compounding method was compared to single-angle plane wave imaging and conventional angular compounding, demonstrating up to 64% reduction in lateral spread in water tank experiments and 52% in prostate phantom studies, with further improvements of 20% and 24%, respectively, over unweighted compounding. The method also achieved significant suppression of reverberation artifacts. These results confirm the method’s effectiveness in enhancing needle delineation and artifact suppression, offering a practical solution compatible with transrectal ultrasound probes for improved brachytherapy needle guidance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".