Two-Step Rigid and Non-Rigid Image Registration for the Alignment of Three-Dimensional Echocardiography Sequences From Multiple Views
Bibliographic record
Abstract
Ultrasound is a widely used imaging modality, which provides continuous real-time imaging of the human heart, brain, liver, and many other organs. Accurate cardiovascular evaluation plays an important role in early disease diagnosis. Real-time 3D echocardiography (RT3DE) imaging allows better three-dimensional (3D) imaging by extracting spatial features along with temporal information, thus improving clinical decision making. Although there have been technological advances, the majority of acquired RT3DE images tend to be of low quality, characterized by the absence of anatomical information, decreased spatial and temporal resolution, speckle noise, and a limited field of view (FOV). By registering RT3DE images obtained from several windows, it is possible to enhance the recognition of structures and achieve a substantial improvement in image quality as well as it is also useful in the fusion of echo images to image the entire heart. This study proposes a fully automatic point-based rigid registration technique, followed by nonrigid B-spline registration, to align four-dimensional (4D) echocardiogram images acquired from various sonographic windows. The methodology was evaluated using scans acquired from seven volunteers. The accuracy of registration was visually and quantitatively assessed by delineating the left ventricle in each scan and computing the Dice score overlap metric and the Hausdorff distance mutual proximity measure between the first scan and the rest. The overall findings demonstrate that the suggested registration method improves image alignment compared to the initial scans, which might be helpful in the fusion of echocardiographic images.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".