Monitoring high-intensity focused ultrasound thermal therapy by ultrasound doppler imaging using twinkling artifact
Bibliographic record
Abstract
High-Intensity Focused Ultrasound (HIFU) is a non-invasive therapeutic modality that uses high-energy acoustic waves to thermally coagulate tissue at the focal region. The Twinkling Artifact (TA) is a color Doppler artifact caused by the acoustic radiation force and the consequent tissue vibration during Doppler imaging. This paper aims to employ TA for real-time detection and monitoring of HIFU-induced lesions. A dataset gathered in a previous study concerning ex vivo porcine tissue samples was used, in which the real-time backscattered radiofrequency signals were acquired before, during, and after HIFU treatment. To investigate the presence of TA in Doppler images, the amplitude of each pixel is considered in the sequence of frames as time-series or slow-time signals. It is shown that the main frequency of slow-time signals represents the Doppler frequency shift. Doppler images were constructed using the maximum frequency from every 10-sample slow-time signal. By constructing Doppler images, the frequency shifts within tissue during HIFU treatment were visually and analytically assessed. Our frequency analysis of RF data confirmed the occurrence of TA during HIFU exposure. Furthermore, a novel method was developed for lesion formation monitoring, with less than a 5% error rate in depth and width measurements for depicting coagulated tissue dimensions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".