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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".