A Novel Multiphysics Optimization-Driven Methodology for the Design of Microwave Ablation Antennas
Bibliographic record
Abstract
Microwave ablation is based on localized heating of biological tissues, enabled by an electromagnetic field. Ablation antennas are commonly designed in a forward approach, where the shape and type of antenna are manipulated to generate elevated temperatures in the target region. However, little attention has been dedicated to designing antennas in an inverse approach to allow controlled synthesis of temperature profiles customized for the application, in a reconfigurable way. Also, existing designs are based on generating a particular electric field or SAR profiles, not accounting for discontinuities in the thermal conductivity of tissue. We propose an inverse multiphysics strategy for source design that involves optimizing the current distribution on the antenna to synthesize a desired temperature profile, while accounting for temperature diffusion. We apply this optimization procedure to a simulated test case in human liver and study the associated fields and temperature profiles, as well as the optimized current distributions required. Our results indicate and quantify the clear advantage of this multiphysics approach compared to electric field focusing in terms of meeting objectives in the thermal domain. We show how the optimized current distributions can be easily implemented in a practical design and integrated into treatment planning in a clinical setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".