A Model-Based Multi-Point Tissue Manipulation for Enhancing Breast Brachytherapy
Bibliographic record
Abstract
In surgical operations, tissue manipulation can be automated to reduce the surgeon’s workload. This work addresses the application of tissue manipulation in breast brachytherapy, which involves manipulating an internal target inside the breast. Unassisted breast brachytherapy causes excessive target movement that reduces seed implantation accuracy. To address this target movement in breast brachytherapy, first, the internal target point will be manipulated accurately and then the brachytherapy needle will be inserted into the immobilized tissue. In this paper, a model-based tissue manipulation method is introduced. To simulate nonlinear large tissue deformation for the first time, a minimum-energy-based deformable tissue solver is utilized. Based on the theory of positive bases, the optimal number of actuators is determined to guarantee controllability of the internal target. A model predictive controller (MPC) is designed to implement multi-point tissue manipulation. A breast phantom is used to test the accuracy of the deformation model and the effectiveness of the proposed control method. The results show that the tissue deformation simulation error is 1.6 mm and the internal target can be regulated with negligible steady-state errors using an MPC controller.
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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 | 0.000 |
| 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 | 0.000 | 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".