Poster — Wed Eve—16: Optoacoustic Detection of Tissue Thermal Damage
Bibliographic record
Abstract
Minimally invasive thermal therapy has been investigated as an alternative treatment modality for solid tumours including breast, liver and prostate. Thermal therapy is typically delivered as a single‐fraction, stand‐alone therapy. It involves heating tissues to greater than 55 C over a period of a few minutes, which results in coagulative necrosis. It can potentially achieve highly conformal 3D coagulation volumes, exhibits sharp demarcation between treated and spared tissues, and tissue effects can be observed and thus potentially controlled in real time. This paper describes a new approach to guiding the progress of thermal therapy using optoacoustics, a technique which combines the high optical contrast and high resolution associated with near‐infrared optical imaging and ultrasound imaging, respectively. In this study, thermal lesions were induced in bovine liver ex vivo via non‐contact single fiber laser heating at 810 nm. Optoacoustic signals were obtained using an optoacoustic imaging system comprised of an Nd:YAG pumped Titanium‐Sapphire laser delivering 6 ns pulses and an annular array of 8, 4 MHz transducers. Optoacoustic signal increased up to 2.5 fold with heating times from 1 to 6 minutes. Furthermore, tissue coagulation was clearly visible in the optoacoustic images compared to the surrounding native tissue. The results demonstrate that optoacoustic signals are sensitive to changes in tissue optical and mechanical properties that occur when tissues are thermally damaged, and, hence, optoacoustic imaging may be a suitable candidate for guiding thermal therapy delivery.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.018 |
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".