A Non-Rigid Image Registration Technique for 3D Ultrasound Carotid Images using a "Twisting and Bending" Model
Bibliographic record
Abstract
Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological treatments if identified early by monitoring carotid plaque changes. Sensitive monitoring of plaque changes in volume and morphology requires that 3D ultrasound (US) images of carotid plaque obtained at different time points be registered and evaluated for change. This registration technique should be non-rigid, since different head positions in image acquisitions cause relative bending and torsion in the neck, producing non-linear deformations between the images. We modeled the movement of the neck using a "twisting and bending model" with only six parameters for non-rigid registration. We used a Mutual Information (MI) based image similarity measure with the Powell optimization method as they have been used effectively in US image registration applications. For evaluation of our algorithm, we acquired 3D US carotid images from three subjects at two different head positions to simulate images acquired at different times. Then, we registered each image set using our "twisting bending model" based non-rigid registration algorithm. We calculated the Mean Registration Error (MRE) between the segmented vessel surfaces in the target image and the registered image using a distance-based error metric. We repeated the experiment with only rigid registration to compare the capabilities of the proposed algorithm in improving registration of 3D carotid US images. The average registration error was 1.03plusmn0.23 mm using our non-rigid registration technique, while it was 1.50plusmn0.50 mm when we applied the rigid registration alone
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".