Bibliographic record
Abstract
In recent years, ultrasound imaging has become an indispensable tool for intra-surgical guidance. Most neuro and spine surgeries, however, are now trending towards minimally invasive approaches. In these surgeries, the entire surgery is performed using endoscopic instruments inserted into a small surgical pathway. Consequently, surgical imaging technologies such as ultrasound, must also adapt to be compatible with these new approaches where they are confined to narrow surgical corridors. We have recently developed a novel, high-resolution, endoscopic ultrasound system specifically for guiding these minimally invasive surgeries. The entire system including the probe, the electronics, and software has been designed and fabricated from the ground up. This imaging platform has recently been approved for a preliminary patient imaging study has already produced promising, first-of-its kind data during brain tumour resection and spinal cord surgeries. This presentation will discuss the challenges associated with developing high frequency (30 MHz) miniaturized micro-arrays and the associated electronic beamformer. The beamformer we have developed is based on parallelized FPGAs, can process data at rates higher than 10Tb/s, and generate frame-rates higher than 1kHz. The beamforming architecture incorporates a hybrid imaging mode that interleaves ultrafast imaging for Doppler, and line-by-line focusing for B-Mode. The Doppler mode is overlayed on the BMode image in real-time.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".