Real-Time Simulation of Ultrasound Image Deformation Using Thin Plate Spline
Bibliographic record
Abstract
Finite element modelling (FEM) has been the primary approach for non-rigid medical image registration and simulating image deformation in virtual models. A notable limitation of FEM is the high computational cost and the challenges associated with model identification in heterogeneous tissue. In this paper, we propose using thin plate spline (TPS) as an alternative for simulating image deformation during ultrasound-guided needle insertion. Unlike FEM, in TPS non-rigid deformations are simulated by moving a point in the mesh to a target location, and recalculating the location of all other points such that bending energy is minimized, potentially making experimental model identification easier.We propose a novel formulation to convert needle-tissue interaction forces, including tissue cutting, friction, and relaxation, into localized nodal displacements that serve as inputs to the TPS model. As the needle moves, it alters the input to the TPS based on its speed, direction, and depth, resulting in a real-time update of the mesh. These updates enable the TPS to adapt continuously, ensuring that the mesh mimics the tissue response. The proposed model is compared against an equivalent FEM model in a series of simulations and experiments in ex-vivo porcine tissue. The results show that both models have similar accuracy with TPS being consistently faster than FEM, with improvements in computational efficiency of above 50%. These results confirm the potential of the proposed method to be integrated into ultrasound-guided surgical simulation to enhance precision with larger meshes while reducing computational burden.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".