Evaluating the utility of intraprocedural 3D TRUS image information in guiding registration for displacement compensation during prostate biopsy
Bibliographic record
Abstract
PURPOSE: In targeted 3D transrectal ultrasound (TRUS)-guided biopsy, patient and prostate movement during the procedure can cause target misalignments that hinder accurate sampling of preplanned suspicious tissue locations. Multiple solutions have been proposed for displacement compensation via registration of intraprocedural TRUS images to a baseline 3D TRUS image acquired at the beginning of the biopsy procedure. While 2D TRUS images are widely used for intraprocedural guidance, some solutions utilize richer intraprocedural images such as bi- or multiplanar TRUS or 3D TRUS, acquired by specialized probes. In this work, the impact of such richer intraprocedural imaging on displacement compensation accuracy was measured to evaluate the tradeoff between cost and complexity of intraprocedural imaging versus improved displacement compensation. METHODS: Baseline and intraprocedural 3D TRUS images were acquired from 29 patients at standard sextant-template biopsy locations. Planes extracted from 3D TRUS images acquired at sextant positions were used to simulate 2D and 3D intraprocedural information available in different potential clinically relevant scenarios for co-registration with the baseline 3D TRUS image. In practice, intraprocedural 3D information can be acquired either via the use of specialized ultrasound probes (e.g., multiplanar or 3D probes) or via axial rotation of a tracked 2D TRUS probe. Registration accuracy was evaluated by calculating the target registration error (TRE) using manually identified homologous intrinsic fiducial markers (microcalcifications). The TRE was analyzed separately at the base, mid-gland and apex regions of the prostate. RESULTS: The results indicate that TRE improved gradually as the number of intraprocedural imaging planes used in registration was increased, implying that 3D TRUS information assisted the registration algorithm to robustly converge to more accurate solutions. The acquisition of a partial volume up to the angle of rotation supported more accurate displacement compensation than acquiring biplane configurations. Additional intraprocedural 3D TRUS image information was more beneficial to registration accuracy in the base and apex regions as compared with the mid-gland region. CONCLUSIONS: While the majority of the registrations using 2D TRUS images provided a clinically desired level of accuracy, intraprocedural 3D imaging helped improve the overall registration accuracy and robustness, especially in the base and apex regions of the prostate. These results are helpful for devising image-based registration methods for displacement compensation when designing 3D TRUS-guided biopsy systems.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".