Experimental Investigation of Perfusion Effects on Heat Transfer in Tissue-Mimicking Phantoms for Cardiac Radiofrequency Ablation
Bibliographic record
Abstract
Radiofrequency (RF) ablation is a widely used minimally invasive procedure for treating cardiac arrhythmias, yet the complex bioheat transfer mechanisms that govern lesion formation remain not entirely understood.This study investigates the thermal behaviour of tissue-mimicking phantoms with and without perfusion channels to quantify the cooling effects of blood flow during cardiac RF ablation.A controlled experimental setup was developed to simulate the thermal conditions of cardiac tissue during ablation, featuring a water bath system maintained at physiological temperature (37°C), a copper heating element to simulate the RF electrode, and embedded thermocouples for precise temperature monitoring.Experiments were conducted on polyacrylamide-based phantoms with embedded microchannels at varying flow rates (15-25 mL/min) and heating element temperatures (325 K-364 K). Results demonstrated that perfusion significantly reduced temperature rise in the phantom, with greater effects observed at measurement points below the perfusion channels (average 22% reduction) compared to points above (average 10% reduction).The temperature gradient between measurement points also decreased with perfusion, indicating more uniform heat distribution.These findings offer experimental validation for perfusion-mediated heat transfer models and provide valuable insights for optimizing RF ablation in clinical practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".