Sketch-based Registration of 3D Cine MRI to 4D flow MRI
Bibliographic record
Abstract
Cardiac 4D Flow magnetic resonance imaging (4D Flow MRI) is a recent powerful technology that uniquely enables in-vivo acquisition of time-varying volumetric blood flow velocity field information in the three spatial dimensions over the cardiac cycle. Hence, 4D Flow MRI has emerged as an important medical diagnostic tool for evaluation of blood flow alteration in the heart chambers and great vessels. A critical requirement for accurate quantification and visualization of blood flow within the different heart chambers (e.g. the left ventricle (LV)) is the accurate anatomical context of cardiac chambers, which is missing in the 4D Flow MRI data. To tackle this problem, recent studies have proposed fusing the 4D Flow data with a complementary anatomical MRI scan (short axis 3D (multiple 2D slices) cine SSFP) through registration. However, since image registration is a non-linear optimization problem, the registration is slow and may not be accurate (e.g. the left ventricle can be incorrectly aligned to the right ventricle). To improve the registration performance and accuracy, localization techniques can be used. In this paper, we propose two sketch-based methods for effective localization of 4D Flow MRI to 3D cine MRI registration. We evaluate these methods and compare them with other localization methods.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".