Three-dimensional modeling and assessment of cardiac adipose tissue distribution.
Bibliographic record
Abstract
Objective: The layer of fat that accumulates around the heart, called cardiac adipose tissue (CAT), can influence the development of coronary disease and is indicative of cardiovascular risk. While volumetric assessment of magnetic resonance imaging (MRI) can quantify CAT, volume alone gives no information about its distribution across the myocardial surface, which may be an important factor in risk assessment. In this study, a three-dimensional (3D) modeling technique is developed and used to quantify the distribution of the CAT across the surface of the heart.Methods: Dixon MRI scans, which produce a registered 3D set of fat-only and water-only images, were acquired in 10 subjects for a study on exercise intervention. A previously developed segmentation algorithm was used to identify the heart and CAT. Extracted contours were used to build 3D models. Procrustes analysis was used to register the heart models and an iterative closest point algorithm was used to register and align the CAT models for calculation of CAT thickness. Rays were cast in directions specified by a spherical parameterization of elevation and azimuthal angles, and intersections of the ray with the CAT surface were used to calculate the thickness at each location. To evaluate the effects of the spherical parameterization on the thickness estimates, a set of synthetic models were created with increasing major-to-minor axis ratios.Results: Based on the validation in the synthetic models, the average error in CAT thickness ranged from 1.25% to 17.3% for increasing major-to-minor axis ratio.Conclusions: A process was developed, based on Dixon MRI data, to provide 3D models of the myocardial surface and the cardiac fat. The models can be used in future segmentation algorithm development and for studies on changes in cardiac fat as a result of various interventions.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".