Optimizing large organ scale micro computed tomography imaging in pig and human hearts using a novel air-drying technique
Bibliographic record
Abstract
Abstract Underlying electrical propagation in the heart and potentially fatal arrhythmia is the cardiac microstructure. Despite the critical role of muscle architecture, a non-destructive approach to examine not only myocyte orientation, but cellular arrangement in to laminar organization is lacking in hearts from translational animal models and humans. X-ray micro computed tomography using contrast enhancing agents achieves three-dimensional images at near-histological resolutions. However, imaging large mammalian hearts presents challenges including X-ray over-attenuation and loss of image contrast. The goal of this study was to rethink tissue pre-treatment to optimize, and benefit from micro computed tomography imaging resolution in large tissues. Whole pig and human hearts were dehydrated and perfused with a tissue reinforcing agent, hexamethyldisilazane, and slowly air-dried. Heart morphology was conserved and temporally stable. This enabled direct air-mounting for micro computed tomography imaging. Moreover, the desiccated tissue density was significantly reduced compared to the initial hydrated state (P=0.04). Three-dimensional image reconstructions of air-dried hearts segmented using a single intensity threshold revealed detailed microstructural architecture of myolaminae. Conversely, one-step segmentation of hearts loaded with contrast agents poorly estimated the gross anatomical morphology of the heart and lacked identification of tissue microarchitecture. Air-drying large mammalian hearts optimizes X-ray imaging of cardiac microstructure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".