Real-time registration by tracking for MR-guided cardiac interventions
Bibliographic record
Abstract
Cardiac interventional procedures such as myocardial stem cell delivery and radiofrequency ablation require a high degree of accuracy and efficiency. Real-time, 2-D MR technology is being developed to guide such procedures; the associated challenges include the relatively low resolution and image quality in real-time images. Real-time MR guidance can be enhanced by acquiring a 4-D (3-D + phase) volume prior to the procedure and aligning it to the 2-D real-time images, so that corresponding features in the prior volume can be integrated into the real-time image visualization. This technique provides spatial context with high resolution and SNR. A left ventricular (LV) myocardial wall contour tracking system was developed to maintain spatial alignment of prior volume images to real-time MR images. Over 9 test images sequences, each comprising 100 frames of simulated respiratory motion, the tracker maintained alignment with a mean displacement error of 1.61mm in a region of interest around the LV, as compared to a mean displacement error of 5.2mm without tracking.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".