Registration of two‐dimensional cardiac images to preprocedural three‐dimensional images for interventional applications
Bibliographic record
Abstract
PURPOSE: To evaluate the accuracy and efficiency of rigid-body registration of two-dimensional fast cine and real-time cardiac images to high-resolution and SNR three-dimensional preprocedural reference volumes for application during MRI-guided interventional procedures. MATERIALS AND METHODS: Mutual information (MI) and correlation ratio (CR) similarity measures were evaluated. The dependence of registration accuracy and efficiency on different resolution and SNR parameters, and also on cardiac-phase differences was evaluated in a porcine model. Two-dimensional images were initially misoriented at distances (d) of 2-10 mm, and rotations of +/-5 degrees about all axes. Registration error and computation time were evaluated, and performance was also assessed visually. RESULTS: The maximum registration error using MI (<2.7 mm and <3.6 degrees ) occurred for d = 10 mm, misrotation of +/-5 degrees , and relative SNR = 1. The computation time was 15 seconds for MI and 10 seconds for CR. CONCLUSION: Registration accuracy was not highly dependent on the relative timing, within the cycle, between the two-dimensional and three-dimensional images. Registration using CR was faster than that using MI, although accuracy was marginally higher with MI. J.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".