Models and measurements of light intensity changes during laser interstitial thermal therapy: implications for optical monitoring of the coagulation boundary location
Bibliographic record
Abstract
We have developed a multi-region spherical Monte Carlo (MC) model to simulate the dynamic changes in light intensity measured during laser interstitial thermal therapy (LITT). Model predictions were validated experimentally in tissue-simulating albumen phantoms with well-characterized optical properties that vary dynamically with LITT in a way similar to tissue. For long treatments (2.5 W, approximately 1800 s), the transient light intensity changes demonstrated better qualitative agreement with a three-region MC model (with an inner layer of fully coagulated optical properties, a middle layer of partially coagulated properties and an outer region of native properties); for short treatments (4 W, approximately 240 s), better qualitative agreement was seen with a two-region MC model (with an inner layer of fully coagulated properties and outer region of native properties). These differences were attributed to differences in coagulation formation during low- and high-powered heating regimes, respectively. At the end of heating, a three-region coagulation zone was observed for both heating schemes. Quantitatively, final light intensity changes at the end of heating were compared with changes predicted by both two- and three-region MC for the same experimentally measured coagulation size and found to agree within approximately 30% for both models. The developed MC model helps lend insight into the nature of thermal coagulation events occurring for low and high power LITT irradiation schemes.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".