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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".