Recent advances in cardiac imaging: emerging use of three-dimensional visualization for analyzing complex cardiovascular anatomy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Technical progress in noninvasive medical imaging continues to enhance diagnosis and intervention, with three-dimensional (3D) imaging emerging as a significant advancement over traditional methods. While 3D visualization is widely used to evaluate a living heart, precise measurement from such images remains challenging. This review describes a new technique named isosurface geometric measurement on volume-rendered images (IMVR), which facilitates accurate 3D measurement of complex cardiovascular anatomy. RECENT FINDINGS: Direct volume rendering provides clear visualization and tissue identification, but the lack of exact spatial boundaries inherently makes measurement of any anatomical feature difficult. However, by superimposing a surface-rendered polygonal mesh (representing isosurface geometry) onto a variably transparent volume image of the heart, IMVR enables significantly easier and more accurate 3D measurement. This technique demonstrates versatility across various cardiovascular, anatomical, and clinical applications, including preinterventional assessment and planning for structural heart diseases, notably expanding 3D imaging's utility toward precision medicine and personalized treatment. SUMMARY: This review article summarizes recent advances in cardiac imaging, highlighting an efficient IMVR technique, which combines volume-rendered images with superimposed surface-rendered image to facilitate accurate 3D measurements of cardiac anatomical features.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".