Real-Time Visualization of 4D Cardiac MR Images Using Graphics Processing Units
Bibliographic record
Abstract
Real-time display of dynamic three-dimensional (3D) cardiac images has important applications in minimally invasive image-guided cardiac surgery and therapy. However, in practice, the high computational cost usually prohibits its application in a real-time medical environment, or else the low image quality does not satisfy the clinical requirements. Surface based organ models or orthogonal image planes are often employed instead, but in the process important intra cardiac data are lost, and intuitive spatial anatomical relationships are eliminated. In this paper, we take advantage of the programmability, parallelism and increased computational precision of modern graphics processing units (GPUs) to build a ray casting based real-time 3D rendering engine, directly running on the graphics vertex and fragment processors. This approach provides enhanced image quality similar to software-based implementations, but its rendering speed is competitive with the traditional but inferior quality slice based volume rendering approaches. In addition, we propose a new dynamic volume texture binding technique, and embedded it into our 3D rendering engine to permit visualize the 4D MR cardiac dataset in real-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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".