Ring navigation: an ultrasound-guided technique using real-time motion compensation for prostate biopsies
Bibliographic record
Abstract
Prostate cancer has the second highest noncutaneous cancer incidence in men. Three-dimensional (3D) transrectal ultrasound (TRUS) fused with a magnetic resonance image (MRI) is used to guide prostate biopsy as an alternative technique to conventional 2D TRUS sextant biopsy. The TRUS-MRI fusion technique can provide intraoperative needle guidance to suspicious cancer tissues identified on MRI, increasing the targeting capabilities of a physician. Currently, 3D TRUS-MR guided biopsy suffers from image and target misalignment caused by various forms of prostate motion. Thus, we previously developed a real-time motion compensation algorithm to align 2D and 3D TRUS images with an update rate around an ultrasound system frame rate. During clinical implementation, observations of image misalignment occurred when obtaining tissue samples near the left and right boundaries of the prostate. To minimize transducer translation on the rectal wall and avoid prostate motion and deformation, we are proposing the use of a 3D model-based ring navigation procedure. This navigation keeps the transducer positioned towards the centroid of the prostate when guiding the tracked biopsy gun to targets. Prostate biopsy was performed on three patients while using real-time motion compensation in the background. Our navigation approach was compared to a conventional 2D TRUS-guided procedure using approximately 20 2D and 3D TRUS image pairs and resulted in median [first quartile, third quartile] registration errors of 2.0 [1.3, 2.5] mm and 3.4 [1.5, 8.2] mm, respectively. Using our navigation approach, registration error and variability were reduced, potentially suggesting a more robust technique when performing continuous motion compensation.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".