An Open-Source Solution for Interactive Acquisition, Processing and Transfer of Interventional Ultrasound Images
Bibliographic record
Abstract
Ultrasound has become a very important modality in image-guided therapy. At present, however, the collection, synchronization and transfer of ultrasonic images are more cumbersome than necessary. This paper presents a reusable solution to these problems. We propose a software package called SynchroGrab, which allows the collection of interventional ultrasound images as well as their synchronization with a stream of pose measurements. The software includes support for an open-interface ultrasound system, namely the Sonix RP, from Ultrasonix (Vancouver, Canada). Using an open-interface system like the Sonix RP allows customization of the imaging process and the capture of the ultrasound images directly from memory without the need for a frame-grabbing card. Pose measurement is currently performed with an Optotrak Certus by Northern Digital (Waterloo, Canada). However, extensibility was a primary goal in the design of this software, so the support of new devices can be achieved simply by sub-classing the relevant base class. SynchroGrab also performs reconstruction of 3D ultrasound volumes from synchronized data streams. Moreover, the recorded images, volumes and tracking information are available for visualization or further processing either directly from the file system or from a network connection compliant with the OpenIGTLink protocol, which is supported by Slicer 3.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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".