Toward high-intensity focused ultrasound lesion quantification using compressive sensing theory
Bibliographic record
Abstract
Compressive sensing theory has in recent years been increasingly used in various pattern recognition applications. Compressive sensing theory makes it possible, under certain assumptions, to recover a signal or an image sampled below the Nyquist sampling limit. In this work, a new application of compressive sensing based on the threshold algorithm, in the area of controlling and monitoring of high-intensity focused ultrasound therapy, was investigated. In this work, a new method of high-intensity focused ultrasound lesion detection is presented based on a modified compressive sensing method in combination with the threshold algorithm and the wavelet transforms. In this study, analysis of the suggested method is performed using two sets of data: simulated and experimental ultrasound radio frequency data. The results of processing the data show that the proposed algorithm results in enhancement of the high-intensity focused ultrasound lesion contrast in comparison with the ultrasound B-mode and standard compressive sensing imaging methods. The results of the study show that the modified compressive sensing method could effectively detect thermal lesions in vitro. Comparing the estimated size of the thermal lesion (8.3 mm × 8.4 mm) using the proposed algorithm with the actual size of that from physical examination (10.1 mm × 9 mm) shows that we could detect high-intensity focused ultrasound thermal lesions with the difference of 0.8 mm × 0.5 mm.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| 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".