Bibliographic record
Abstract
Spatial compound imaging via beam-steering aims to improve image quality through signal averaging. However, compounding techniques are vulnerable to speed-of-sound and refraction distortions in non-homogeneous tissue. We have developed a system to perform image-based non-rigid registration in real time. The goal is to increase image quality by improving the alignment of the ultrasound frames before compounding. Frames are acquired by a PC-based ultrasound machine (Ultrasonix Inc, Vancouver, Canada), and transmitted to a Windows-based workstation through a high-speed network. Robust image-to-image registration (warping) is performed using block-based estimation of local shifts and thin-plate spline interpolation. Compound images are computed as a rolling average of the nine most recent warped frames. The procedure runs at 20 frames per second on a dual-processor Xeon workstation, demonstrating the feasibility of sophisticated real-time image processing on a standard PC platform. High speed is achieved through algorithm refinements, approximations in speed-critical sections, and low-level optimizations. The result is a fully-automatic real-time spatial compounding system with a demonstrated improvement in image quality. Tests of registration accuracy were performed on simulated data with realistic speckle patterns, using a 10% speed-of-sound variation and an 8° beam-steering angle. The average misalignment across the image was reduced by 70%, from of 0.22 mm to 0.07 mm; in the deepest parts of the image, alignment was improved by 91%. Improved quality is demonstrated on images of a human forearm, which show visibly improved edge sharpness. This work is one of a series of projects demonstrating the ability of a new open-architecture ultrasound system.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".