Adaptive spatial compounding for improving ultrasound images of the epidural space
Bibliographic record
Abstract
Epidural anesthesia can be a difficult procedure, especially for inexperienced physicians. The use of ultrasound imaging can help by depicting the location of the epidural space to choose the needle trajectory appropriately. Anatomical features in the lower back are not always clearly visible because of speckle poor reflection from structures at certain angles, and shadows from bony surfaces. Spatial compounding has the potential to reduce speckle and emphasize structures by averaging a number of images taken at different isonation angles. However, the beam-steered images are not perfectly aligned due to non-constant speed of sound causing refraction errors. This means compounding can blur features. A non-rigid registration method, called warping, shifts each block of pixels of the beam-steered images in order to find the best alignment to the reference image without beam-steering. By applying warping, the features become sharper after compounding. To emphasize features further, edge detection is also applied to the individual images in order to select the best features for compounding. The warping and edge detection parameters are calculated in real-time for each acquired image. In order to reduce computational complexity, linear prediction of the warping vectors is used. The algorithm is tested on a phantom of the lower back with a linear probe. Qualitative comparisons are made among the original plus combinations of compounding, warping, edge detection and linear prediction. The linear gradient and Laplacian of a Gaussian are used to quantitatively assess the visibility of the bone boundaries and ligamentum flavum on the processed images. The results show a significant improvement in quality.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".