P6D-5 Enhancement of Bone Surface Visualization Using Ultrasound Radio-Frequency Signals
Bibliographic record
Abstract
Detection of bone surfaces in ultrasound images would be useful for ultrasound guided orthopedic surgery, biopsy and brachytherapy. However, bones are often poorly visualized with conventional B-mode ultrasound due to speckle, shadowing, reverberation and other artifacts in tissue. In this paper, we investigate two new techniques for the enhancement of bone surface visualization using ultrasound radio frequency (RF) signals, instead of using conventional B-mode images. The first approach uses strain imaging or elastography, and the second method directly monitors the reflected power of the RF signal. The potential of the proposed methods is demonstrated through phantom and in vivo experiments. Experimental results show that the two methods produce satisfactory contrast between bone surfaces and soft tissue, and are suitable for real-time applications. The good performance of these approaches suggests that they have promise in a clinical setting.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".