Directional Log-Gabor filtering on the pre-beamformed channel data to enhance hyper echoic structures
Bibliographic record
Abstract
One of the challenges in ultrasound bone imaging is related to the specular reflection in the soft tissue-bone interface. Since the transmit ultrasound beam is not necessarily perpendicular to the bone surface, the reflected ultrasound signals from bones are often tilted in the channel data. Even though the amount of tilting may not be large, this can still result in cancellation of the bone signals during beamforming, weakening the bone interfaces. In this paper, we have developed a novel beamforming method which applies directional Log-Gabor filtering in channel data prior to the beamforming. The channel data are first delay compensated, and then the directional LogGabor filter are used to enhance signals within +/-15 degrees of horizontal direction (6 dB FWHM). The filtered channel data are then masked based on tensor based-image analysis to suppress noise. Vertebrae phantom studies (10 data sets) and in vivo volunteer studies (30 data sets) show the average contrast ratio (CR) for the bones is improved from 0.57 in delay and sum beamforming (DAS) to 0.91 in the proposed method. Weak bone interfaces such as the spinal process can also be visualized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".