Real-Time Control of Nanoparticle-Mediated Thermal Therapy Using Photoacoustic Imaging
Bibliographic record
Abstract
OBJECTIVE: This work aims to determine whether photoacoustic (PA) thermometry from a commercially available PA imaging system can be used to control the temperature in nanoparticle-mediated thermal therapies. METHODS: The PA imaging system was interfaced to obtain PA images while scanning ex-vivo tissue. These images were then used to obtain temperature maps in real-time during heating. Validation and calibration of the PA thermometry were done using a fluoroptic thermometer. This thermometer was also used to develop and tune a software-based proportional integral derivative (PID) controller. Finally, a PA-based PID closed-loop controller was used to control gold nanorod (GNR) mediated laser therapy. RESULTS: The use of GNRs substantially enhanced laser heating; the temperature rise increased 7-fold by injecting a GNR solution with a concentration of 0.029 mg/mL. The control experiments showed that the desired temperature could be achieved and maintained at a targeted location in the ex-vivo tissue. The steady-state mean absolute deviations (MAD) from the targeted temperature during control were between 0.16 [Formula: see text] and 0.5 [Formula: see text], depending on the experiment. CONCLUSION: It was possible to control hyperthermia treatments using a software-based PID controller and a commercial PA imaging system. SIGNIFICANCE: The monitoring and control of the temperature in thermal-based therapies are important for assuring a prescribed temperature to the target tissue while minimizing the temperature of the surrounding healthy tissue. This easily implemented non-invasive control system will facilitate the realization of a broad range of hyperthermia treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".