Robust registration and phantom validation of mechatronics-assisted MRI-guided needle delivery for prostate focal laser ablation therapy
Bibliographic record
Abstract
Magnetic resonance imaging (MRI)-guided prostate focal laser ablation (FLA) therapy shows potential as a minimally invasive treatment method for localized prostate cancer, which minimizes overtreatment of surrounding structures, thereby improving quality of life. We previously developed an MRI-compatible mechatronic guidance system capable of needle positioning within an open-air and in-bore MRI environment. In comparison to open-air testing, an increased error was reported from in-bore experiments, suggesting the effects of image distortion, fiducial localization, and registration error may impact its accuracy. In this paper, we describe the design of an improved registration multi-fiducial for the robust registration of the mechatronic system to MRI, and comparison and validation of MRI-guided needle delivery to virtual targets (simulating localized focal zones) in tissue-mimicking prostate phantoms. The multi-fiducial structure is composed of thirty-six MR-spheres arranged across an extensive volume. Mechatronics-assisted MRI-guided needle delivery (N =10) to virtual targets were evaluated with tissue-mimicking phantoms. 3T MRI images were acquired for registration, the mechatronic system was remotely actuated and needle insertion was performed, then verification images were acquired. The needle tip and needle trajectory error were quantified between the planned and actual trajectories. Our preliminary results show significant improvements in needle targeting with the improved registration fiducial with an FLA ablation region radius of 2.0 mm within 95% confidence. Improvements in robust registration show potential to enable accurate mechatronics-assisted MRI-guided needle delivery for FLA therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".