3D Transrectal Ultrasound-Guided Prostate Biopsy Platform Integrated with Prostate-Specific PET: Registration and Accuracy Evaluation
Bibliographic record
Abstract
This study presents a proof-of-concept validation of a trans-perineal prostate biopsy platform integrating three-dimensional (3D) transrectal ultrasound (TRUS) with a prostatespecific PET (P-PET) system. The platform combines the anatomical imaging of 3D TRUS with the functional imaging of $\mathbf{P}$-PET, enabling accurate lesion targeting. Unlike conventional PET systems, the P-PET design uses two planar detectors positioned near the prostate, improving resolution and reducing radiation dose. To enable real-time anatomical guidance, the system incorporates a motorized 3D TRUS unit, a tracking arm, and a mechatronic needle guidance device. The system includes modules for volumetric ultrasound acquisition, deep learning-based segmentation, dual-modality registration, and trajectory planning. Registration was performed using a calibration grid with ten non-collinear fiducials, yielding a mean Fiducial Registration Error (FRE) of 0.699 mm with a standard deviation of 0.416 mm. Target Registration Errors (TREs), based on ten internal target fiducials, were below 0.1 mm in all directions, with 95 percent confidence intervals including zero, indicating no directional bias. Needle targeting accuracy was evaluated using a tissue-mimicking agar phantom with five spherical inclusions. Biopsy paths were planned in 3D TRUS and executed under 2D TRUS guidance. Post-fire imaging was used to segment the needle track, and the 3D Needle Targeting Error (NTE) was 0.84 mm with a standard deviation of 0.32 mm, confirming submillimeter accuracy. This work introduces the first integrated 3D TRUS and P-PET-guided prostate biopsy system. Preliminary results demonstrate accurate spatial registration and needle guidance. Future work includes integration with the actual P-PET hardware and clinical validation using PSMA radiotracer in phantom or patient trials.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".