3D ultrasound system for needle guidance during high-dose-rate interstitial gynecologic brachytherapy implant placement procedures
Bibliographic record
Abstract
During high-dose-rate (HDR) interstitial brachytherapy of gynecologic malignancies, precise placement of multiple needles is necessary to provide optimal dose to the tumor while avoiding overexposing nearby healthy organs, such as the bladder and rectum. Needles are currently placed based on preoperative imaging and clinical examination but there is currently no standard for intraoperative image guidance. We propose the use of a three-dimensional (3D) ultrasound (US) system incorporating three scanning geometries: 3D transrectal US (TRUS), 360° 3D sidefire transvaginal US (TVUS), and 3D endfire TVUS, to provide an accessible and versatile tool for intraoperative image guidance during interstitial gynecologic brachytherapy. Images are generated in 12 - 20 s by rotating a conventional two-dimensional US probe, providing a reconstructed 3D image immediately following acquisition. Studies of needles in patient images show mean differences in needle positions of 3.82 ± 1.86 mm and 2.36 ± 0.97 mm in TRUS and sidefire TVUS, respectively, when compared to the clinical x-ray computed tomography (CT) images. A proof-of-concept phantom study of the endfire TVUS mode demonstrated a mean positional difference of 1.91 ± 0.24 mm. Additionally, an automatic needle segmentation tool was tested on a 360° 3D TVUS patient image resulting in a mean angular difference of 0.44 ± 0.19 ° and mean positional difference of 0.78 ± 0.17 mm when compared to manually segmented needles. The implementation of 3D US image guidance during HDR interstitial gynecologic brachytherapy provides a versatile intraoperative system with the potential for improved implant quality and reduced risk to nearby organs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".