Design and fabrication of a low-frequency (1-3 MHz) ultrasound transducer for accurate placement of screw implants in the spine
Bibliographic record
Abstract
In 2012 approximately 800,000 spinal fusion surgeries were performed in the United States, requiring the insertion of screws into the pedicles. Their exact placement is critical and made complex due to limited visibility of the spine, continuous bleeding in the exposed regions, and variability in morphologies. The alarmingly high rate of screw misplacements (up to 20%) reported in the literature is of major concern since such misplacements can place the surrounding vital structures at risk. A potential guidance method for determining the best screw trajectory is by the use of real-time ultrasound imaging similar to that used for intravascular imaging. An endovascular transducer could be inserted into the pedicle to image the anatomy from within and identify bone boundaries. A major challenge of imaging within bone is high signal attenuation. The rapid increase of attenuation with frequency requires much lower frequencies (1-3 MHz) than those used in intravascular imaging. This study describes the custom design and fabrication of 2 MHz ultrasound probes (3.5 mm diameter/ 11 Fr) for pedicle screw guidance. Three transducer designs are explored to provide improved sensitivity and signal to noise ratio, compared to the previously tested transducer within the pedicle. Experimental measurements are compared with the results obtained using various simulation tools. The work reported in this paper represents the first stage in our ultimate goal of developing a 32- element phased array that is capable of generating a radial B-mode image.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".