Development of a 3D carotid atlas for quantification of local volume change
Bibliographic record
Abstract
Stroke is among the leading causes of death and disability throughout the world. Carotid atherosclerosis is a focal disease predominantly occurring at bifurcations, and for this reason, local progression/regression measurements of atherosclerosis allow for more sensitive detection of treatment effect than global measurements, such as total vessel wall volume (VWV). Vessel-wall-plus-plaque thickness (VWT) change has been developed to characterize local changes and has shown to be sensitive to treatment effect, but is unable to isolate changes in individual plaque components. In this work, we propose to quantify longitudinal voxel-by-voxel plaque-and-vessel-wall volume change (ΔVVol) and represent the ΔVVol distribution on a 3D standardized atlas. Such representation allows for quantitative comparison across patients and of the measurements obtained for the same patient at different time points. We introduced a 3D non-rigid registration framework to register the carotid ultrasound images acquired at baseline and a follow-up imaging session for each patient. A 3D volume equipped with voxel-by-voxel ΔVVol was obtained by taking the divergence of the displacement field obtained in non-rigid registration. This 3D volume was uniformly sampled in the vessel wall, and the ΔVVol distribution for each patient was represented in a 3D standardized map. The proposed 3D standardized ΔVVol map allows for the characterization of feature changes on a voxel-by-voxel basis that are masked in VWT quantification. This tool has the potential to further improve the sensitivity in treatment evaluation already attained by VWT quantification.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".