Coupled thermo-electro-mechanical models for thermal ablation of biological tissues and heat relaxation time effects
Bibliographic record
Abstract
Thermal ablation is a widely applied electrosurgical process in medical treatment of soft biological tissues. Numerical modeling and simulations play an important role in prediction of temperature distribution and damage volume during the treatment planning stage of associated therapies. In this contribution we report a coupled thermo-electro-mechanical model, accounting for heat relaxation time, for more accurate and precise prediction of the temperature distribution, tissue deformation and damage volume during the thermal ablation of biological tissues. Finite element solutions are obtained for most widely used percutaneous thermal ablative techniques, viz., radiofrequency ablation (RFA) and microwave ablation (MWA). Importantly, both tissue expansion and shrinkage have been considered for modeling the tissue deformation in the coupled model of high temperature thermal ablation. The coupled model takes into account the non-Fourier effects, considering both single-phase-lag (SPL) and dual-phase-lag (DPL) models of bio-heat transfer. The temperature-dependent electrical and thermal parameters, damage-dependent blood perfusion rate and phase change effect accounting for tissue vaporization have been accounted for obtaining more clinically relevant model. The proposed model predictions are found to be in good agreement against the temperature distribution and damage volume reported by previous experimental studies. The numerical simulation results revealed that the non-Fourier effects cause a decrease in the predicted temperature distribution, tissue deformation and damage volume during the high temperature thermal ablative procedures. Furthermore, the effects of different magnitudes of phase lags of the heat flux and temperature gradient on the predicted treatment outcomes of the considered thermal ablative modalities are also quantified and discussed in detail.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".