3D-3D rigid registration of echocardiographic images obtained from apical window
Bibliographic record
Abstract
Echocardiography is widely used in clinics to assess heart function and structure. However, it suffers from a limited field of view. There are no algorithms that enable perfect alignment of 3D echocardiographic images captured from various angles to improve image quality and broaden the field of view. This study proposes an accelerated sequential Monte Carlo (SMC) algorithm for 3D-3D rigid registration of transthoracic echocardiographic images with significant and limited overlap taken from the apical window that is robust to the noise and intensity variation in ultrasound images. The SMC algorithm estimates the translational and rotational components of the rigid transform through an iterative process and requires an initial approximation of the translation and rotation limits. We perform registration in two ways: image-based registration computes the transform to align the end-diastolic frame of the apical nonstandard image to the apical standard image and applies the same transform to all frames of the cardiac cycle, whereas mask-based registration approach uses the binary masks of the left ventricle in the same way. The SMC and exhaustive search (EX) algorithms were evaluated for 4D temporal sequences recorded from 7 volunteers who participated in a study conducted at the Mazankowski Alberta Heart Institute. The evaluations demonstrate that the mask-based approach of the accelerated SMC yielded a Dice score value of 0.819 ± 0.045 for the left ventricle and gained 16.7 × speedup compared to the CPU version of the SMC algorithm. The mask-based accelerated SMC-based approach performs well compared to the other approaches in terms of accuracy and computational time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".