Validation of Diffeomorphic Registration on Cine Cardiac MR via Direct Frame-to-Frame Comparison with HARP Tracking on Tagged MR
Bibliographic record
Abstract
Cardiac magnetic resonance (MR) tissue tagging offers an excellent solution for tracking deformation and is considered the reference standard for the quantification of strain. However, due to the requirements for a dedicated acquisition sequence and post-processing software, tagged MR acquisitions are performed much less frequently in routine clinical practice than the anatomical cine MR sequence. Using tagged MR as the reference standard, this study proposes an approach to evaluate a diffeomorphic image registration algorithm applied on cine MR images to compute the cardiac deformation. In contrast to previous evaluation methods that compared the final results, such as strain, computed from cine and tagged MR sequences, the proposed method performs a direct frame-to-frame comparison in the evaluation. To overcome the problem of misalignment between the tagged and cine MR images, the proposed approach performs transformations to and from the two-dimensional image pixel coordinates and three-dimensional space using the meta-information encoded in the MR images. Linear temporal interpolation is performed using the frame acquisition time since the last R-wave peak value of the electrocardiogram signal recorded in the meta-information. Several statistic measures are computed and reported for the registration error using the Euclidean distances between the corresponding set of points obtained using cine and tagged MR images.
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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 | 0.005 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".