A haptic-enhanced 3D real-time interactive needle insertion simulation for prostate brachytherapy
Bibliographic record
Abstract
A virtual reality based surgical simulation can improve the accuracy and quality of prostate brachytherapy by facilitating surgeon training, rehearsal, and intra-operative assistance. In this paper, we describe a prototype 3D realtime interactive simulation environment for needle insertion and seed implantation for prostate brachytherapy. A restricted 3D ChainMail method, derived from the original 3D ChainMail method based on our modification, was used to account for dynamic soft tissue deformation during needle insertion. We improved the neighbor-searching algorithm for the original 3D ChainMail method to enable a complete search for any objects including strict-concave. A direct manipulation model for needle-tissue interaction was implemented. A haptic feedback has also been provided to enhance realism and training outcome. For simplicity and efficacy, we have adopted a distributed system structure functionally incorporating two software modules: visual simulation module and haptic simulation module. The simulation was demonstrated using four key steps of the brachytherapy procedure: 1) specification of seed positions inside the prostate; 2) placement of a needle at a specified entry point and trajectory; 3) insertion of the needle into the prostate consisting of two basic sub-steps: membrane contraction and penetration insertion; and, 4) retraction of the needle after seed implantation. The preliminary results of the simulation are promising.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.004 | 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".